TY  - JOUR
AU  - Babović, Zoran
AU  - Bajat, Branislav
AU  - Đokić, Vladan
AU  - Đorđević, Filip
AU  - Drašković, Dražen
AU  - Filipović, Nenad
AU  - Furht, Borko
AU  - Gačić, Nikola
AU  - Ikodinović, Igor
AU  - Ilić, Marija
AU  - Irfanoglu, Ayhan
AU  - Jelenković, Branislav
AU  - Kartelj, Aleksandar
AU  - Klimeck, Gerhard
AU  - Korolija, Nenad
AU  - Kotlar, Miloš
AU  - Kovačević, Miloš
AU  - Kuzmanović, Vladan
AU  - Marinković, Marko
AU  - Marković, Slobodan
AU  - Mendelson, Avi
AU  - Milutinović, Veljko
AU  - Nešković, Aleksandar
AU  - Nešković, Nataša
AU  - Mitić, Nenad
AU  - Nikolić, Boško
AU  - Novoselov, Konstantin
AU  - Prakash, Arun
AU  - Ratković, Ivan
AU  - Stojadinović, Zoran
AU  - Ustyuzhanin, Andrey
AU  - Zak, Stan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3113
AB  - This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).
PB  - Springer
T2  - Journal of Big Data
T1  - Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems
VL  - 10
DO  - 10.1186/s40537-023-00731-6
ER  - 

@article{
author = "Babović, Zoran and Bajat, Branislav and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and Furht, Borko and Gačić, Nikola and Ikodinović, Igor and Ilić, Marija and Irfanoglu, Ayhan and Jelenković, Branislav and Kartelj, Aleksandar and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Marinković, Marko and Marković, Slobodan and Mendelson, Avi and Milutinović, Veljko and Nešković, Aleksandar and Nešković, Nataša and Mitić, Nenad and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Ratković, Ivan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan",
year = "2023",
abstract = "This article presents a taxonomy and represents a repository of open problems in computing for numerically and logically intensive problems in a number of disciplines that have to synergize for the best performance of simulation-based feasibility studies on nature-oriented engineering in general and civil engineering in particular. Topics include but are not limited to: Nature-based construction, genomics supporting nature-based construction, earthquake engineering, and other types of geophysical disaster prevention activities, as well as the studies of processes and materials of interest for the above. In all these fields, problems are discussed that generate huge amounts of Big Data and are characterized with mathematically highly complex Iterative Algorithms. In the domain of applications, it has been stressed that problems could be made less computationally demanding if the number of computing iterations is made smaller (with the help of Artificial Intelligence or Conditional Algorithms), or if each computing iteration is made shorter in time (with the help of Data Filtration and Data Quantization). In the domain of computing, it has been stressed that computing could be made more powerful if the implementation technology is changed (Si, GaAs, etc.…), or if the computing paradigm is changed (Control Flow, Data Flow, etc.…).",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems",
volume = "10",
doi = "10.1186/s40537-023-00731-6"
}

Babović, Z., Bajat, B., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., Furht, B., Gačić, N., Ikodinović, I., Ilić, M., Irfanoglu, A., Jelenković, B., Kartelj, A., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Marinković, M., Marković, S., Mendelson, A., Milutinović, V., Nešković, A., Nešković, N., Mitić, N., Nikolić, B., Novoselov, K., Prakash, A., Ratković, I., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00731-6

Babović Z, Bajat B, Đokić V, Đorđević F, Drašković D, Filipović N, Furht B, Gačić N, Ikodinović I, Ilić M, Irfanoglu A, Jelenković B, Kartelj A, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Marinković M, Marković S, Mendelson A, Milutinović V, Nešković A, Nešković N, Mitić N, Nikolić B, Novoselov K, Prakash A, Ratković I, Stojadinović Z, Ustyuzhanin A, Zak S. Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00731-6 .

Babović, Zoran, Bajat, Branislav, Đokić, Vladan, Đorđević, Filip, Drašković, Dražen, Filipović, Nenad, Furht, Borko, Gačić, Nikola, Ikodinović, Igor, Ilić, Marija, Irfanoglu, Ayhan, Jelenković, Branislav, Kartelj, Aleksandar, Klimeck, Gerhard, Korolija, Nenad, Kotlar, Miloš, Kovačević, Miloš, Kuzmanović, Vladan, Marinković, Marko, Marković, Slobodan, Mendelson, Avi, Milutinović, Veljko, Nešković, Aleksandar, Nešković, Nataša, Mitić, Nenad, Nikolić, Boško, Novoselov, Konstantin, Prakash, Arun, Ratković, Ivan, Stojadinović, Zoran, Ustyuzhanin, Andrey, Zak, Stan, "Research in computing‑intensive simulations for nature‑oriented civil‑engineering and related scientific fields, using machine learning and big data: an overview of open problems" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00731-6 . .

TY  - JOUR
AU  - Babović, Zoran
AU  - Bajat, Branislav
AU  - Barac, Dusan
AU  - Bengin, Vesna
AU  - Đokić, Vladan
AU  - Đorđević, Filip
AU  - Drašković, Dražen
AU  - Filipović, Nenad
AU  - French, Stephan
AU  - Furht, Borko
AU  - Ilić, Marija
AU  - Irfanoglu, Ayhan
AU  - Kartelj, Aleksandar
AU  - Kilibarda, Milan
AU  - Klimeck, Gerhard
AU  - Korolija, Nenad
AU  - Kotlar, Miloš
AU  - Kovačević, Miloš
AU  - Kuzmanović, Vladan
AU  - Lehn, Jean-Marie
AU  - Madić, Dejan
AU  - Marinković, Marko
AU  - Mateljević, Miodrag
AU  - Mendelson, Avi
AU  - Mesinger, Fedor
AU  - Milovanović, Gradimir
AU  - Milutinović, Veljko
AU  - Mitić, Nenad
AU  - Nešković, Aleksandar
AU  - Nešković, Nataša
AU  - Nikolić, Boško
AU  - Novoselov, Konstantin
AU  - Prakash, Arun
AU  - Protić, Jelica
AU  - Ratković, Ivan
AU  - Rios, Diego
AU  - Shechtman, Dan
AU  - Stojadinović, Zoran
AU  - Ustyuzhanin, Andrey
AU  - Zak, Stan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3114
AB  - This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.
PB  - Springer
T2  - Journal of Big Data
T1  - Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains
VL  - 10
DO  - 10.1186/s40537-023-00730-7
ER  - 

@article{
author = "Babović, Zoran and Bajat, Branislav and Barac, Dusan and Bengin, Vesna and Đokić, Vladan and Đorđević, Filip and Drašković, Dražen and Filipović, Nenad and French, Stephan and Furht, Borko and Ilić, Marija and Irfanoglu, Ayhan and Kartelj, Aleksandar and Kilibarda, Milan and Klimeck, Gerhard and Korolija, Nenad and Kotlar, Miloš and Kovačević, Miloš and Kuzmanović, Vladan and Lehn, Jean-Marie and Madić, Dejan and Marinković, Marko and Mateljević, Miodrag and Mendelson, Avi and Mesinger, Fedor and Milovanović, Gradimir and Milutinović, Veljko and Mitić, Nenad and Nešković, Aleksandar and Nešković, Nataša and Nikolić, Boško and Novoselov, Konstantin and Prakash, Arun and Protić, Jelica and Ratković, Ivan and Rios, Diego and Shechtman, Dan and Stojadinović, Zoran and Ustyuzhanin, Andrey and Zak, Stan",
year = "2023",
abstract = "This article describes a teaching strategy that synergizes computing and management, aimed at the running of complex projects in industry and academia, in the areas of civil engineering, physics, geosciences, and a number of other related fields. The course derived from this strategy includes four parts: (a) Computing with a selected set of modern paradigms—the stress is on Control Flow and Data Flow computing paradigms, but paradigms conditionally referred to as Energy Flow and Diffusion Flow are also covered; (b) Project management that is holistic—the stress is on the wide plethora of issues spanning from the preparation of project proposals, all the way to incorporation activities to follow after the completion of a successful project; (c) Examples from past research and development experiences—the stress is on experiences of leading experts from academia and industry; (d) Student projects that stimulate creativity—the stress is on methods that educators could use to induce and accelerate the creativity of students in general. Finally, the article ends with selected pearls of wisdom that could be treated as suggestions for further elaboration.",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains",
volume = "10",
doi = "10.1186/s40537-023-00730-7"
}

Babović, Z., Bajat, B., Barac, D., Bengin, V., Đokić, V., Đorđević, F., Drašković, D., Filipović, N., French, S., Furht, B., Ilić, M., Irfanoglu, A., Kartelj, A., Kilibarda, M., Klimeck, G., Korolija, N., Kotlar, M., Kovačević, M., Kuzmanović, V., Lehn, J., Madić, D., Marinković, M., Mateljević, M., Mendelson, A., Mesinger, F., Milovanović, G., Milutinović, V., Mitić, N., Nešković, A., Nešković, N., Nikolić, B., Novoselov, K., Prakash, A., Protić, J., Ratković, I., Rios, D., Shechtman, D., Stojadinović, Z., Ustyuzhanin, A.,& Zak, S.. (2023). Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00730-7

Babović Z, Bajat B, Barac D, Bengin V, Đokić V, Đorđević F, Drašković D, Filipović N, French S, Furht B, Ilić M, Irfanoglu A, Kartelj A, Kilibarda M, Klimeck G, Korolija N, Kotlar M, Kovačević M, Kuzmanović V, Lehn J, Madić D, Marinković M, Mateljević M, Mendelson A, Mesinger F, Milovanović G, Milutinović V, Mitić N, Nešković A, Nešković N, Nikolić B, Novoselov K, Prakash A, Protić J, Ratković I, Rios D, Shechtman D, Stojadinović Z, Ustyuzhanin A, Zak S. Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00730-7 .

Babović, Zoran, Bajat, Branislav, Barac, Dusan, Bengin, Vesna, Đokić, Vladan, Đorđević, Filip, Drašković, Dražen, Filipović, Nenad, French, Stephan, Furht, Borko, Ilić, Marija, Irfanoglu, Ayhan, Kartelj, Aleksandar, Kilibarda, Milan, Klimeck, Gerhard, Korolija, Nenad, Kotlar, Miloš, Kovačević, Miloš, Kuzmanović, Vladan, Lehn, Jean-Marie, Madić, Dejan, Marinković, Marko, Mateljević, Miodrag, Mendelson, Avi, Mesinger, Fedor, Milovanović, Gradimir, Milutinović, Veljko, Mitić, Nenad, Nešković, Aleksandar, Nešković, Nataša, Nikolić, Boško, Novoselov, Konstantin, Prakash, Arun, Protić, Jelica, Ratković, Ivan, Rios, Diego, Shechtman, Dan, Stojadinović, Zoran, Ustyuzhanin, Andrey, Zak, Stan, "Teaching computing for complex problems in civil engineering and geosciences using big data and machine learning: synergizing four different computing paradigms and four different management domains" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00730-7 . .

TY  - JOUR
AU  - Antonijević, Ognjen
AU  - Jelić, Slobodan
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3098
AB  - This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.
PB  - Springer
T2  - Journal of Big Data
T1  - Transfer learning approach based on satellite image time series for the crop classification problem
VL  - 10
DO  - 10.1186/s40537-023-00735-2
ER  - 

@article{
author = "Antonijević, Ognjen and Jelić, Slobodan and Bajat, Branislav and Kilibarda, Milan",
year = "2023",
abstract = "This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.",
publisher = "Springer",
journal = "Journal of Big Data",
title = "Transfer learning approach based on satellite image time series for the crop classification problem",
volume = "10",
doi = "10.1186/s40537-023-00735-2"
}

Antonijević, O., Jelić, S., Bajat, B.,& Kilibarda, M.. (2023). Transfer learning approach based on satellite image time series for the crop classification problem. in Journal of Big Data
Springer., 10.
https://doi.org/10.1186/s40537-023-00735-2

Antonijević O, Jelić S, Bajat B, Kilibarda M. Transfer learning approach based on satellite image time series for the crop classification problem. in Journal of Big Data. 2023;10.
doi:10.1186/s40537-023-00735-2 .

Antonijević, Ognjen, Jelić, Slobodan, Bajat, Branislav, Kilibarda, Milan, "Transfer learning approach based on satellite image time series for the crop classification problem" in Journal of Big Data, 10 (2023),
https://doi.org/10.1186/s40537-023-00735-2 . .

TY  - JOUR
AU  - Srejić, Tanja
AU  - Manojlović, Sanja
AU  - Sibinović, Mikica
AU  - Bajat, Branislav
AU  - Novković, Ivan
AU  - Milošević, V.Marko
AU  - Carević, Ivana
AU  - Todosijević, Mirjana
AU  - Sedlak, G.Marko
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3079
AB  - The erosion potential model was applied to estimate the soil erosion status of rural settlements during the years 1971 and 2011. We used univariate and bivariate local Moran’s I indices to detect and visualize the spatial clustering of settlements with respect to changes in erosion intensity and agricultural land use, as well as their mutual spatial correlation. The study area was differentiated into four statistically significant clusters using the calculated bivariate local Moran’s I indices. The statistical analysis examined the two largest clusters, i.e., the high–high and low–low clusters, and the results of the research indicate that the first four principal components explained 70.50% and 73.47% of the total variance, respectively. In the high–high cluster, the low rates of erosion reduction (average Index Z = 98) in the most significant types of rural settlements were determined according to demographic indicators (i.e., the higher population vitality and population density, the smaller share of the old population and the lower average age of the population) and the large proportion of arable land and Neogene sediments. In the low–low cluster, high erosion reduction rates were detected (average index Z = 64). In this cluster, the more statistically significant influence of natural conditions in combination with demographic–agrarian processes (i.e., the larger share of the old population, the higher average age of the population, the lower vitality index and deagrarization) were decisive factors in changing erosion intensity.
PB  - MDPI
T2  - Agriculture
T1  - Agricultural Land Use Changes as a Driving Force of Soil Erosion in the Velika Morava River Basin, Serbia
IS  - 4
VL  - 13
DO  - 10.3390/agriculture13040778
ER  - 

@article{
author = "Srejić, Tanja and Manojlović, Sanja and Sibinović, Mikica and Bajat, Branislav and Novković, Ivan and Milošević, V.Marko and Carević, Ivana and Todosijević, Mirjana and Sedlak, G.Marko",
year = "2023",
abstract = "The erosion potential model was applied to estimate the soil erosion status of rural settlements during the years 1971 and 2011. We used univariate and bivariate local Moran’s I indices to detect and visualize the spatial clustering of settlements with respect to changes in erosion intensity and agricultural land use, as well as their mutual spatial correlation. The study area was differentiated into four statistically significant clusters using the calculated bivariate local Moran’s I indices. The statistical analysis examined the two largest clusters, i.e., the high–high and low–low clusters, and the results of the research indicate that the first four principal components explained 70.50% and 73.47% of the total variance, respectively. In the high–high cluster, the low rates of erosion reduction (average Index Z = 98) in the most significant types of rural settlements were determined according to demographic indicators (i.e., the higher population vitality and population density, the smaller share of the old population and the lower average age of the population) and the large proportion of arable land and Neogene sediments. In the low–low cluster, high erosion reduction rates were detected (average index Z = 64). In this cluster, the more statistically significant influence of natural conditions in combination with demographic–agrarian processes (i.e., the larger share of the old population, the higher average age of the population, the lower vitality index and deagrarization) were decisive factors in changing erosion intensity.",
publisher = "MDPI",
journal = "Agriculture",
title = "Agricultural Land Use Changes as a Driving Force of Soil Erosion in the Velika Morava River Basin, Serbia",
number = "4",
volume = "13",
doi = "10.3390/agriculture13040778"
}

Srejić, T., Manojlović, S., Sibinović, M., Bajat, B., Novković, I., Milošević, V.Marko, Carević, I., Todosijević, M.,& Sedlak, G.Marko. (2023). Agricultural Land Use Changes as a Driving Force of Soil Erosion in the Velika Morava River Basin, Serbia. in Agriculture
MDPI., 13(4).
https://doi.org/10.3390/agriculture13040778

Srejić T, Manojlović S, Sibinović M, Bajat B, Novković I, Milošević V, Carević I, Todosijević M, Sedlak G. Agricultural Land Use Changes as a Driving Force of Soil Erosion in the Velika Morava River Basin, Serbia. in Agriculture. 2023;13(4).
doi:10.3390/agriculture13040778 .

Srejić, Tanja, Manojlović, Sanja, Sibinović, Mikica, Bajat, Branislav, Novković, Ivan, Milošević, V.Marko, Carević, Ivana, Todosijević, Mirjana, Sedlak, G.Marko, "Agricultural Land Use Changes as a Driving Force of Soil Erosion in the Velika Morava River Basin, Serbia" in Agriculture, 13, no. 4 (2023),
https://doi.org/10.3390/agriculture13040778 . .

TY  - CONF
AU  - Kovačević, Miloš
AU  - Bursać, Petar
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2804
AB  - Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.
C3  - 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia
T1  - AI in Agriculture
UR  - https://hdl.handle.net/21.15107/rcub_grafar_2804
ER  - 

@conference{
author = "Kovačević, Miloš and Bursać, Petar and Bajat, Branislav and Kilibarda, Milan",
year = "2022",
abstract = "Soil organic carbon represents the main nutrient source for crop yields, which is of great importance to agricultural production. This research investigates the usage of a transfer learning-based neural network model to predict SOC values from geochemical soil parameters. The results on datasets representing five European countries showed that the model was able to capture the valuable information contained in grassland soil samples when predicting the SOC values in cropland areas.",
journal = "1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia",
title = "AI in Agriculture",
url = "https://hdl.handle.net/21.15107/rcub_grafar_2804"
}

Kovačević, M., Bursać, P., Bajat, B.,& Kilibarda, M.. (2022). AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia.
https://hdl.handle.net/21.15107/rcub_grafar_2804

Kovačević M, Bursać P, Bajat B, Kilibarda M. AI in Agriculture. in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia. 2022;.
https://hdl.handle.net/21.15107/rcub_grafar_2804 .

Kovačević, Miloš, Bursać, Petar, Bajat, Branislav, Kilibarda, Milan, "AI in Agriculture" in 1st Serbian International Conference on Applied Artificial Intelligence (SICAAI), Kragujevac, Serbia (2022),
https://hdl.handle.net/21.15107/rcub_grafar_2804 .

TY  - JOUR
AU  - Bursać, Petar
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2801
AB  - Soil organic carbon (SOC) is a vital component for sustainable agricultural
production. This research investigates the transfer learning-based neural
network model to improve classical machine learning estimation of SOC
values from other geochemical and physical soil parameters. The results on
datasets based on LUCAS data from 2015 showed that the Instance-based
transfer learning model captured the valuable information contained in different
source domains (cropland and grassland) of soil samples when estimating the
SOC values in arable cropland areas. The effects of using transfer learning are
more pronounced in the case of different source (grassland) and target
(cropland) domains. Obtained results indicate that the transfer learning (TL)
approach provides better or at least equal output results compared to the
classical machine learning procedure. The proposed TL methodology could be
used to generate a pedotransfer function (PTF) for target domains with
described samples and unknown related PTF outputs if the described
samples with known related PTF outputs from a different geographic or
similar land class source domain are available
T2  - Frontiers in Environmental Science
T1  - Instance-based transfer learning for soil organic carbon estimation
DO  - https://doi.org/10.3389/fenvs.2022.1003918
ER  - 

@article{
author = "Bursać, Petar and Kovačević, Miloš and Bajat, Branislav",
year = "2022",
abstract = "Soil organic carbon (SOC) is a vital component for sustainable agricultural
production. This research investigates the transfer learning-based neural
network model to improve classical machine learning estimation of SOC
values from other geochemical and physical soil parameters. The results on
datasets based on LUCAS data from 2015 showed that the Instance-based
transfer learning model captured the valuable information contained in different
source domains (cropland and grassland) of soil samples when estimating the
SOC values in arable cropland areas. The effects of using transfer learning are
more pronounced in the case of different source (grassland) and target
(cropland) domains. Obtained results indicate that the transfer learning (TL)
approach provides better or at least equal output results compared to the
classical machine learning procedure. The proposed TL methodology could be
used to generate a pedotransfer function (PTF) for target domains with
described samples and unknown related PTF outputs if the described
samples with known related PTF outputs from a different geographic or
similar land class source domain are available",
journal = "Frontiers in Environmental Science",
title = "Instance-based transfer learning for soil organic carbon estimation",
doi = "https://doi.org/10.3389/fenvs.2022.1003918"
}

Bursać, P., Kovačević, M.,& Bajat, B.. (2022). Instance-based transfer learning for soil organic carbon estimation. in Frontiers in Environmental Science.
https://doi.org/https://doi.org/10.3389/fenvs.2022.1003918

Bursać P, Kovačević M, Bajat B. Instance-based transfer learning for soil organic carbon estimation. in Frontiers in Environmental Science. 2022;.
doi:https://doi.org/10.3389/fenvs.2022.1003918 .

Bursać, Petar, Kovačević, Miloš, Bajat, Branislav, "Instance-based transfer learning for soil organic carbon estimation" in Frontiers in Environmental Science (2022),
https://doi.org/https://doi.org/10.3389/fenvs.2022.1003918 . .

TY  - JOUR
AU  - Manojlović, Sanja
AU  - Srejić, Tanja
AU  - Sibinović, Mikica
AU  - Milošević, V. Marko
AU  - Bajat, Branislav
AU  - Kostadinov, Stanimir
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2803
AB  - Sediment transport time series have shown a severe change in the suspended sediment load transported by the Velika Morava River (Republic of Serbia) during the last few decades. The research objectives of this study were to determine the suspended sediment trends, and to assess the impact of variations in precipitation and human activities on the suspended sediment load.
The causes and timing of this severe decrease were analyzed, and the results show that there has been a significant sudden shift downwards for the suspended sediment load (p<0.0001) during the research period. The change points for sediment load were very similar and the transition years all ranged between 1982 and 1984. The combined effects of precipitation and human activities are responsible for the decrease in the suspended sediment load, with human activity being the most active factor in
the sediment regime changes. The contribution rate for human activities amounts to 87.7–91.9%, while precipitation explains 8.1–12.3% of the reduction in the suspended sediment load. The processes of deagrarization and depopulation had an influence on the sediment load decrease in the study area. The results of the spatial autocorrelation analysis of rural settlements showed
that the reduction in sediment was due to the process of depopulation and the large reduction in the amount of arable land in rural areas and settlements. The changes in sediment regimes were also influenced by soil and water conservation programs
T2  - Arabian Journal of Geosciences
T1  - Impact of precipitation and human activities on suspended sediment transport load in the Velika Morava River Basin (Serbia)
VL  - 15
DO  - https://doi.org/10.1007/s12517-022-10475-x
ER  - 

@article{
author = "Manojlović, Sanja and Srejić, Tanja and Sibinović, Mikica and Milošević, V. Marko and Bajat, Branislav and Kostadinov, Stanimir",
year = "2022",
abstract = "Sediment transport time series have shown a severe change in the suspended sediment load transported by the Velika Morava River (Republic of Serbia) during the last few decades. The research objectives of this study were to determine the suspended sediment trends, and to assess the impact of variations in precipitation and human activities on the suspended sediment load.
The causes and timing of this severe decrease were analyzed, and the results show that there has been a significant sudden shift downwards for the suspended sediment load (p<0.0001) during the research period. The change points for sediment load were very similar and the transition years all ranged between 1982 and 1984. The combined effects of precipitation and human activities are responsible for the decrease in the suspended sediment load, with human activity being the most active factor in
the sediment regime changes. The contribution rate for human activities amounts to 87.7–91.9%, while precipitation explains 8.1–12.3% of the reduction in the suspended sediment load. The processes of deagrarization and depopulation had an influence on the sediment load decrease in the study area. The results of the spatial autocorrelation analysis of rural settlements showed
that the reduction in sediment was due to the process of depopulation and the large reduction in the amount of arable land in rural areas and settlements. The changes in sediment regimes were also influenced by soil and water conservation programs",
journal = "Arabian Journal of Geosciences",
title = "Impact of precipitation and human activities on suspended sediment transport load in the Velika Morava River Basin (Serbia)",
volume = "15",
doi = "https://doi.org/10.1007/s12517-022-10475-x"
}

Manojlović, S., Srejić, T., Sibinović, M., Milošević, V. M., Bajat, B.,& Kostadinov, S.. (2022). Impact of precipitation and human activities on suspended sediment transport load in the Velika Morava River Basin (Serbia). in Arabian Journal of Geosciences, 15.
https://doi.org/https://doi.org/10.1007/s12517-022-10475-x

Manojlović S, Srejić T, Sibinović M, Milošević VM, Bajat B, Kostadinov S. Impact of precipitation and human activities on suspended sediment transport load in the Velika Morava River Basin (Serbia). in Arabian Journal of Geosciences. 2022;15.
doi:https://doi.org/10.1007/s12517-022-10475-x .

Manojlović, Sanja, Srejić, Tanja, Sibinović, Mikica, Milošević, V. Marko, Bajat, Branislav, Kostadinov, Stanimir, "Impact of precipitation and human activities on suspended sediment transport load in the Velika Morava River Basin (Serbia)" in Arabian Journal of Geosciences, 15 (2022),
https://doi.org/https://doi.org/10.1007/s12517-022-10475-x . .

TY  - JOUR
AU  - Petronijević, Marija
AU  - Višnjevac, Nenad
AU  - Praščević, Nataša
AU  - Bajat, Branislav
PY  - 2021
UR  - https://www.mdpi.com/2220-9964/10/5/297
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2415
AB  - The growth of densely populated urban areas has caused traditional cadastral registration systems to face many difficulties in representing complex and multilevel property situations on 2D maps. These challenges, combined with the rapid development of 3D technologies, have forced the research and progress of 3D cadastre systems. The aim of this study is to investigate how a Building Information Model (BIM) can be used as a data source for the Land Administration Domain Model (LADM) based 3D cadastre system, and how that process can be improved. The Industry Foundation Classes (IFC) format and the LADM-based model were selected because both are international open standards that have a significant impact in their own domain. The data sample for the 3D cadastre system was extracted from a BIM model. The paper proposes an IFC format extension which makes it possible to define 3D geometry according to the LADM standard. In order to demonstrate this extension, the West 65 residential and business complex, Belgrade, was selected as a case study. The IFC format extension presented here is a step towards data harmonization between BIM in the IFC format and 3D cadastre systems; it should provide more suitable data in the current IFC schema and enable easy data flow between BIM projects and 3D cadastral data.
PB  - MDPI
T2  - ISPRS International Journal of Geo-Information
T1  - The Extension of IFC For Supporting 3D Cadastre LADM Geometry
IS  - 5
SP  - 297
VL  - 10
DO  - 10.3390/ijgi10050297
ER  - 

@article{
author = "Petronijević, Marija and Višnjevac, Nenad and Praščević, Nataša and Bajat, Branislav",
year = "2021",
abstract = "The growth of densely populated urban areas has caused traditional cadastral registration systems to face many difficulties in representing complex and multilevel property situations on 2D maps. These challenges, combined with the rapid development of 3D technologies, have forced the research and progress of 3D cadastre systems. The aim of this study is to investigate how a Building Information Model (BIM) can be used as a data source for the Land Administration Domain Model (LADM) based 3D cadastre system, and how that process can be improved. The Industry Foundation Classes (IFC) format and the LADM-based model were selected because both are international open standards that have a significant impact in their own domain. The data sample for the 3D cadastre system was extracted from a BIM model. The paper proposes an IFC format extension which makes it possible to define 3D geometry according to the LADM standard. In order to demonstrate this extension, the West 65 residential and business complex, Belgrade, was selected as a case study. The IFC format extension presented here is a step towards data harmonization between BIM in the IFC format and 3D cadastre systems; it should provide more suitable data in the current IFC schema and enable easy data flow between BIM projects and 3D cadastral data.",
publisher = "MDPI",
journal = "ISPRS International Journal of Geo-Information",
title = "The Extension of IFC For Supporting 3D Cadastre LADM Geometry",
number = "5",
pages = "297",
volume = "10",
doi = "10.3390/ijgi10050297"
}

Petronijević, M., Višnjevac, N., Praščević, N.,& Bajat, B.. (2021). The Extension of IFC For Supporting 3D Cadastre LADM Geometry. in ISPRS International Journal of Geo-Information
MDPI., 10(5), 297.
https://doi.org/10.3390/ijgi10050297

Petronijević M, Višnjevac N, Praščević N, Bajat B. The Extension of IFC For Supporting 3D Cadastre LADM Geometry. in ISPRS International Journal of Geo-Information. 2021;10(5):297.
doi:10.3390/ijgi10050297 .

Petronijević, Marija, Višnjevac, Nenad, Praščević, Nataša, Bajat, Branislav, "The Extension of IFC For Supporting 3D Cadastre LADM Geometry" in ISPRS International Journal of Geo-Information, 10, no. 5 (2021):297,
https://doi.org/10.3390/ijgi10050297 . .

TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
AU  - Bajat, Branislav
PY  - 2021
UR  - https://www.nature.com/articles/s41597-021-00901-2
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2355
AB  - We produced the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for 2000–2019, named MeteoSerbia1km. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea-level pressure, and total precipitation. In addition to daily summaries, we produced monthly and annual summaries, and daily, monthly, and annual long-term means. Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology, based on using the nearest observations and distances to them as spatial covariates, together with environmental covariates to make a random forest model. The accuracy of the MeteoSerbia1km daily dataset was assessed using nested 5-fold leave-location-out cross-validation. All temperature variables and sea-level pressure showed high accuracy, although accuracy was lower for total precipitation, due to the discontinuity in its spatial distribution. MeteoSerbia1km was also compared with the E-OBS dataset with a coarser resolution: both datasets showed similar coarse-scale patterns for all daily meteorological variables, except for total precipitation. As a result of its high resolution, MeteoSerbia1km is suitable for further environmental analyses.
PB  - Springer Nature
T2  - Scientific Data
T1  - A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation
IS  - 123
VL  - 8
DO  - 10.1038/s41597-021-00901-2
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Protić, Dragutin and Bajat, Branislav",
year = "2021",
abstract = "We produced the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for 2000–2019, named MeteoSerbia1km. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea-level pressure, and total precipitation. In addition to daily summaries, we produced monthly and annual summaries, and daily, monthly, and annual long-term means. Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology, based on using the nearest observations and distances to them as spatial covariates, together with environmental covariates to make a random forest model. The accuracy of the MeteoSerbia1km daily dataset was assessed using nested 5-fold leave-location-out cross-validation. All temperature variables and sea-level pressure showed high accuracy, although accuracy was lower for total precipitation, due to the discontinuity in its spatial distribution. MeteoSerbia1km was also compared with the E-OBS dataset with a coarser resolution: both datasets showed similar coarse-scale patterns for all daily meteorological variables, except for total precipitation. As a result of its high resolution, MeteoSerbia1km is suitable for further environmental analyses.",
publisher = "Springer Nature",
journal = "Scientific Data",
title = "A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation",
number = "123",
volume = "8",
doi = "10.1038/s41597-021-00901-2"
}

Sekulić, A., Kilibarda, M., Protić, D.,& Bajat, B.. (2021). A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation. in Scientific Data
Springer Nature., 8(123).
https://doi.org/10.1038/s41597-021-00901-2

Sekulić A, Kilibarda M, Protić D, Bajat B. A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation. in Scientific Data. 2021;8(123).
doi:10.1038/s41597-021-00901-2 .

Sekulić, Aleksandar, Kilibarda, Milan, Protić, Dragutin, Bajat, Branislav, "A high-resolution daily gridded meteorological dataset for Serbia made by Random Forest Spatial Interpolation" in Scientific Data, 8, no. 123 (2021),
https://doi.org/10.1038/s41597-021-00901-2 . .

TY  - JOUR
AU  - Bajat, Branislav
AU  - Antonijević, Ognjen
AU  - Kilibarda, Milan
AU  - Sekulić, Aleksandar
AU  - Luković, Jelena
AU  - Doljak, Dejan
AU  - Burić, Dragan
PY  - 2020
UR  - https://spatium.rs/index.php/home/article/view/257
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2414
AB  - The  assessment  of  the  potential  use  of  renewable  energy  resources  requires  reliable  and  precise  data  inputs  for  sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids  of  potential  insolation  and  solar  duration  in  order  to  determine  the  location  of  potential  solar  power  plants  in  Montenegro.  Grids  with  a  25-m  spatial  resolution  of  potential  solar  radiation  and  duration  were  produced  based  on  observational  records  and  publicly  available  high-resolution  digital  elevation  model  provided  by  the  European  Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m² and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy.
PB  - Institut za argitekturu i urbanizam Srbije
T2  - Spatium
T1  - Space-time high-resolution data of the potential insolation and solar duration for Montenegro
IS  - 44
DO  - 10.2298/SPAT2044045B
ER  - 

@article{
author = "Bajat, Branislav and Antonijević, Ognjen and Kilibarda, Milan and Sekulić, Aleksandar and Luković, Jelena and Doljak, Dejan and Burić, Dragan",
year = "2020",
abstract = "The  assessment  of  the  potential  use  of  renewable  energy  resources  requires  reliable  and  precise  data  inputs  for  sustainable energy planning on a regional, national and local scale. In this study, we examine high spatial resolution grids  of  potential  insolation  and  solar  duration  in  order  to  determine  the  location  of  potential  solar  power  plants  in  Montenegro.  Grids  with  a  25-m  spatial  resolution  of  potential  solar  radiation  and  duration  were  produced  based  on  observational  records  and  publicly  available  high-resolution  digital  elevation  model  provided  by  the  European  Environment Agency. These results could be further used for the estimation and selection of a specific location for solar panels. With an average annual potential insolation of 1800 kWh/m² and solar duration of over 2000 h per year for most of its territory, Montenegro is one of the European countries with the highest potential for the development, production, and consumption of solar energy.",
publisher = "Institut za argitekturu i urbanizam Srbije",
journal = "Spatium",
title = "Space-time high-resolution data of the potential insolation and solar duration for Montenegro",
number = "44",
doi = "10.2298/SPAT2044045B"
}

Bajat, B., Antonijević, O., Kilibarda, M., Sekulić, A., Luković, J., Doljak, D.,& Burić, D.. (2020). Space-time high-resolution data of the potential insolation and solar duration for Montenegro. in Spatium
Institut za argitekturu i urbanizam Srbije.(44).
https://doi.org/10.2298/SPAT2044045B

Bajat B, Antonijević O, Kilibarda M, Sekulić A, Luković J, Doljak D, Burić D. Space-time high-resolution data of the potential insolation and solar duration for Montenegro. in Spatium. 2020;(44).
doi:10.2298/SPAT2044045B .

Bajat, Branislav, Antonijević, Ognjen, Kilibarda, Milan, Sekulić, Aleksandar, Luković, Jelena, Doljak, Dejan, Burić, Dragan, "Space-time high-resolution data of the potential insolation and solar duration for Montenegro" in Spatium, no. 44 (2020),
https://doi.org/10.2298/SPAT2044045B . .

TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
AU  - Perčec-Tadić, Melita
AU  - Bajat, Branislav
PY  - 2020
UR  - https://link.springer.com/article/10.1007/s00704-019-03077-3
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2257
AB  - High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.
PB  - Springer Nature
T2  - Theoretical and Applied Climatology
T1  - Spatio-temporal regression kriging model of mean daily temperature for Croatia
EP  - 114
SP  - 101
VL  - 140
DO  - https://doi.org/10.1007/s00704-019-03077-3
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Protić, Dragutin and Perčec-Tadić, Melita and Bajat, Branislav",
year = "2020",
abstract = "High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.",
publisher = "Springer Nature",
journal = "Theoretical and Applied Climatology",
title = "Spatio-temporal regression kriging model of mean daily temperature for Croatia",
pages = "114-101",
volume = "140",
doi = "https://doi.org/10.1007/s00704-019-03077-3"
}

Sekulić, A., Kilibarda, M., Protić, D., Perčec-Tadić, M.,& Bajat, B.. (2020). Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology
Springer Nature., 140, 101-114.
https://doi.org/https://doi.org/10.1007/s00704-019-03077-3

Sekulić A, Kilibarda M, Protić D, Perčec-Tadić M, Bajat B. Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology. 2020;140:101-114.
doi:https://doi.org/10.1007/s00704-019-03077-3 .

Sekulić, Aleksandar, Kilibarda, Milan, Protić, Dragutin, Perčec-Tadić, Melita, Bajat, Branislav, "Spatio-temporal regression kriging model of mean daily temperature for Croatia" in Theoretical and Applied Climatology, 140 (2020):101-114,
https://doi.org/https://doi.org/10.1007/s00704-019-03077-3 . .

TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Heuvelink, Gerard B. M.
AU  - Nikolić, Mladen
AU  - Bajat, Branislav
PY  - 2020
UR  - https://www.mdpi.com/2072-4292/12/10/1687
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1973
AB  - For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.
PB  - MDPI
T2  - Remote Sensing
T1  - Random Forest Spatial Interpolation
IS  - 10
SP  - 1687
VL  - 12
DO  - 10.3390/rs12101687
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Heuvelink, Gerard B. M. and Nikolić, Mladen and Bajat, Branislav",
year = "2020",
abstract = "For many decades, kriging and deterministic interpolation techniques, such as inverse distance weighting and nearest neighbour interpolation, have been the most popular spatial interpolation techniques. Kriging with external drift and regression kriging have become basic techniques that benefit both from spatial autocorrelation and covariate information. More recently, machine learning techniques, such as random forest and gradient boosting, have become increasingly popular and are now often used for spatial interpolation. Some attempts have been made to explicitly take the spatial component into account in machine learning, but so far, none of these approaches have taken the natural route of incorporating the nearest observations and their distances to the prediction location as covariates. In this research, we explored the value of including observations at the nearest locations and their distances from the prediction location by introducing Random Forest Spatial Interpolation (RFSI). We compared RFSI with deterministic interpolation methods, ordinary kriging, regression kriging, Random Forest and Random Forest for spatial prediction (RFsp) in three case studies. The first case study made use of synthetic data, i.e., simulations from normally distributed stationary random fields with a known semivariogram, for which ordinary kriging is known to be optimal. The second and third case studies evaluated the performance of the various interpolation methods using daily precipitation data for the 2016–2018 period in Catalonia, Spain, and mean daily temperature for the year 2008 in Croatia. Results of the synthetic case study showed that RFSI outperformed most simple deterministic interpolation techniques and had similar performance as inverse distance weighting and RFsp. As expected, kriging was the most accurate technique in the synthetic case study. In the precipitation and temperature case studies, RFSI mostly outperformed regression kriging, inverse distance weighting, random forest, and RFsp. Moreover, RFSI was substantially faster than RFsp, particularly when the training dataset was large and high-resolution prediction maps were made.",
publisher = "MDPI",
journal = "Remote Sensing",
title = "Random Forest Spatial Interpolation",
number = "10",
pages = "1687",
volume = "12",
doi = "10.3390/rs12101687"
}

Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M.,& Bajat, B.. (2020). Random Forest Spatial Interpolation. in Remote Sensing
MDPI., 12(10), 1687.
https://doi.org/10.3390/rs12101687

Sekulić A, Kilibarda M, Heuvelink GBM, Nikolić M, Bajat B. Random Forest Spatial Interpolation. in Remote Sensing. 2020;12(10):1687.
doi:10.3390/rs12101687 .

Sekulić, Aleksandar, Kilibarda, Milan, Heuvelink, Gerard B. M., Nikolić, Mladen, Bajat, Branislav, "Random Forest Spatial Interpolation" in Remote Sensing, 12, no. 10 (2020):1687,
https://doi.org/10.3390/rs12101687 . .

TY  - JOUR
AU  - Višnjevac, Nenad
AU  - Mihajlović, Rajica
AU  - Šoškić, Mladen
AU  - Cvijetinović, Željko
AU  - Bajat, Branislav
PY  - 2019
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1761
AB  - 3D cadastral systems are more complex than traditional cadastral systems and they require more complex technical solutions and innovative use of developing technologies. Regarding data integrity and data consistency, 3D cadastral data should be maintained by a Database Management System (DBMS). Furthermore, there are still challenges regarding visualization of 3D cadastral data. A prototype of the 3D cadastral system based on a NoSQL database and a JavaScript application for 3D visualization is designed and tested in order to investigate the possibilities of using new technical solutions. It is assumed that this approach, with further development, could be a good basis for the development of a modern 3D cadastral system. MongoDB database is used for storing data and Cesium JavaScript library is used for 3D visualization. The system uses an LADM (Land Administration Domain Model) based data model. Additionally, script languages, libraries, application programming interfaces (APIs), software and data formats are used for the system development. The case study is based on the real cadastral data. The underground object and building units located below and above the ground level are used to test the proposed data model and the system’s functionality. The proposed system needs further development in order to provide full support to a modern 3D cadastral system. However, it allows maintenance of 3D cadastral data and basic 3D visualization with the interactive approach.
T1  - Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application
VL  - 8
DO  - 10.3390/ijgi8050227
ER  - 

@article{
author = "Višnjevac, Nenad and Mihajlović, Rajica and Šoškić, Mladen and Cvijetinović, Željko and Bajat, Branislav",
year = "2019",
abstract = "3D cadastral systems are more complex than traditional cadastral systems and they require more complex technical solutions and innovative use of developing technologies. Regarding data integrity and data consistency, 3D cadastral data should be maintained by a Database Management System (DBMS). Furthermore, there are still challenges regarding visualization of 3D cadastral data. A prototype of the 3D cadastral system based on a NoSQL database and a JavaScript application for 3D visualization is designed and tested in order to investigate the possibilities of using new technical solutions. It is assumed that this approach, with further development, could be a good basis for the development of a modern 3D cadastral system. MongoDB database is used for storing data and Cesium JavaScript library is used for 3D visualization. The system uses an LADM (Land Administration Domain Model) based data model. Additionally, script languages, libraries, application programming interfaces (APIs), software and data formats are used for the system development. The case study is based on the real cadastral data. The underground object and building units located below and above the ground level are used to test the proposed data model and the system’s functionality. The proposed system needs further development in order to provide full support to a modern 3D cadastral system. However, it allows maintenance of 3D cadastral data and basic 3D visualization with the interactive approach.",
title = "Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application",
volume = "8",
doi = "10.3390/ijgi8050227"
}

Višnjevac, N., Mihajlović, R., Šoškić, M., Cvijetinović, Ž.,& Bajat, B.. (2019). Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application. , 8.
https://doi.org/10.3390/ijgi8050227

Višnjevac N, Mihajlović R, Šoškić M, Cvijetinović Ž, Bajat B. Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application. 2019;8.
doi:10.3390/ijgi8050227 .

Višnjevac, Nenad, Mihajlović, Rajica, Šoškić, Mladen, Cvijetinović, Željko, Bajat, Branislav, "Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application", 8 (2019),
https://doi.org/10.3390/ijgi8050227 . .

TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
AU  - Perčec-Tadić, Melita
AU  - Bajat, Branislav
PY  - 2019
UR  - https://link.springer.com/article/10.1007/s00704-019-03077-3
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1847
AB  - High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.
PB  - Springer
T2  - Theoretical and Applied Climatology
T1  - Spatio-temporal regression kriging model of mean daily temperature for Croatia
DO  - 10.1007/s00704-019-03077-3
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Protić, Dragutin and Perčec-Tadić, Melita and Bajat, Branislav",
year = "2019",
abstract = "High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.",
publisher = "Springer",
journal = "Theoretical and Applied Climatology",
title = "Spatio-temporal regression kriging model of mean daily temperature for Croatia",
doi = "10.1007/s00704-019-03077-3"
}

Sekulić, A., Kilibarda, M., Protić, D., Perčec-Tadić, M.,& Bajat, B.. (2019). Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology
Springer..
https://doi.org/10.1007/s00704-019-03077-3

Sekulić A, Kilibarda M, Protić D, Perčec-Tadić M, Bajat B. Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology. 2019;.
doi:10.1007/s00704-019-03077-3 .

Sekulić, Aleksandar, Kilibarda, Milan, Protić, Dragutin, Perčec-Tadić, Melita, Bajat, Branislav, "Spatio-temporal regression kriging model of mean daily temperature for Croatia" in Theoretical and Applied Climatology (2019),
https://doi.org/10.1007/s00704-019-03077-3 . .

TY  - JOUR
AU  - Nikitović, Vladimir
AU  - Arsenović, Daniela
AU  - Sekulić, Aleksandar
AU  - Bajat, Branislav
PY  - 2019
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1840
AB  - Gaps in comprehension of demographic change in the region of ex-Yugoslavia after 1990, caused by a lack of reliable data series, frequent change of borders, and distinctive historical and cultural tradition in comparison to other post-communist societies, motivated us to contribute to the understanding of the spatial diffusion of recent profound fertility changes in South-Eastern Europe. We analysed changes in the spatial pattern and distribution of typical fertility indicators of the second demographic transition at the sub-national level in Serbia in order to find out whether these demographic shifts could be interpreted to be similar to those in Central and Eastern Europe. We found that differences in economic, historical, and cultural development between sub-regions of the country strongly affect spatial patterns of fertility change. Also, this paper suggests that the sub-regions forerunners of the first demographic transition could be considered as the cores of diffusion for the second demographic transition.
T2  - AUC Geographica
T1  - Is the Second Demographic Transition a useful framework for understanding the spatial patterns of fertility change in Serbia at the beginning of the 21st century?
EP  - 167
IS  - 2
SP  - 152
VL  - 54
DO  - 10.14712/23361980.2019.14
ER  - 

@article{
author = "Nikitović, Vladimir and Arsenović, Daniela and Sekulić, Aleksandar and Bajat, Branislav",
year = "2019",
abstract = "Gaps in comprehension of demographic change in the region of ex-Yugoslavia after 1990, caused by a lack of reliable data series, frequent change of borders, and distinctive historical and cultural tradition in comparison to other post-communist societies, motivated us to contribute to the understanding of the spatial diffusion of recent profound fertility changes in South-Eastern Europe. We analysed changes in the spatial pattern and distribution of typical fertility indicators of the second demographic transition at the sub-national level in Serbia in order to find out whether these demographic shifts could be interpreted to be similar to those in Central and Eastern Europe. We found that differences in economic, historical, and cultural development between sub-regions of the country strongly affect spatial patterns of fertility change. Also, this paper suggests that the sub-regions forerunners of the first demographic transition could be considered as the cores of diffusion for the second demographic transition.",
journal = "AUC Geographica",
title = "Is the Second Demographic Transition a useful framework for understanding the spatial patterns of fertility change in Serbia at the beginning of the 21st century?",
pages = "167-152",
number = "2",
volume = "54",
doi = "10.14712/23361980.2019.14"
}

Nikitović, V., Arsenović, D., Sekulić, A.,& Bajat, B.. (2019). Is the Second Demographic Transition a useful framework for understanding the spatial patterns of fertility change in Serbia at the beginning of the 21st century?. in AUC Geographica, 54(2), 152-167.
https://doi.org/10.14712/23361980.2019.14

Nikitović V, Arsenović D, Sekulić A, Bajat B. Is the Second Demographic Transition a useful framework for understanding the spatial patterns of fertility change in Serbia at the beginning of the 21st century?. in AUC Geographica. 2019;54(2):152-167.
doi:10.14712/23361980.2019.14 .

Nikitović, Vladimir, Arsenović, Daniela, Sekulić, Aleksandar, Bajat, Branislav, "Is the Second Demographic Transition a useful framework for understanding the spatial patterns of fertility change in Serbia at the beginning of the 21st century?" in AUC Geographica, 54, no. 2 (2019):152-167,
https://doi.org/10.14712/23361980.2019.14 . .

TY  - CONF
AU  - Višnjevac, Nenad
AU  - Mihajlović, Rajica
AU  - Šoškić, Mladen
AU  - Cvijetinović, Željko
AU  - Marošan, Stevan
AU  - Bajat, Branislav
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1642
AB  - The  real  estate  cadastre  in  the  Republic  of  Serbia  is  based  on  2D  cadastral  maps  and procedures  that  do  not  support  unequivocal  registration  and  visualization  of  complex  3D property  situations  or  complex  objects  located  on/below  several  parcels,  especially  in  urban areas.   Within   this   study,   we   analyzed   and   documented   specific   situations   concerning registration   challenges   for   the   current   cadastral   system   in   the   Republic   of   Serbia.Furthermore,  the  analysis  of  additional  functionalities  which  will  enable  overcoming  the limitations of the current cadastre in the short to the medium-termtime period is represented. Themain  objective  is  to  use  the  current  cadastral  data  and  procedures  as  far  as  possible  in order  to  keep  the  transition  smoother  and  economicallyfeasible. Having  in  mindthis objective,thevariation  ofthe hybridapproach  as  the  solution  for  Serbian  3D  real  estate cadastrewas  analyzed.  One  of  the  preliminary  assumptions  of  this  research  is  that  it  is possible to develop a systemthat is simple enoughforimplementationand maintenance,but at  the  same  comprehensive  enough to  overcome  the  difficulties ofthe  current  real  estate cadastre.Within  the  case study,3D  objects  based  ondata  currently  provided  by  licensed surveying agenciesare presented.
PB  - FIG (International Federation of Surveyors), Delft
C3  - Proceedings of the 6th International FIG 3D Cadastre Workshop
T1  - Developing Serbian 3D Cadastre System - Challenges and Directions
EP  - 406
SP  - 383
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1642
ER  - 

@conference{
author = "Višnjevac, Nenad and Mihajlović, Rajica and Šoškić, Mladen and Cvijetinović, Željko and Marošan, Stevan and Bajat, Branislav",
year = "2018",
abstract = "The  real  estate  cadastre  in  the  Republic  of  Serbia  is  based  on  2D  cadastral  maps  and procedures  that  do  not  support  unequivocal  registration  and  visualization  of  complex  3D property  situations  or  complex  objects  located  on/below  several  parcels,  especially  in  urban areas.   Within   this   study,   we   analyzed   and   documented   specific   situations   concerning registration   challenges   for   the   current   cadastral   system   in   the   Republic   of   Serbia.Furthermore,  the  analysis  of  additional  functionalities  which  will  enable  overcoming  the limitations of the current cadastre in the short to the medium-termtime period is represented. Themain  objective  is  to  use  the  current  cadastral  data  and  procedures  as  far  as  possible  in order  to  keep  the  transition  smoother  and  economicallyfeasible. Having  in  mindthis objective,thevariation  ofthe hybridapproach  as  the  solution  for  Serbian  3D  real  estate cadastrewas  analyzed.  One  of  the  preliminary  assumptions  of  this  research  is  that  it  is possible to develop a systemthat is simple enoughforimplementationand maintenance,but at  the  same  comprehensive  enough to  overcome  the  difficulties ofthe  current  real  estate cadastre.Within  the  case study,3D  objects  based  ondata  currently  provided  by  licensed surveying agenciesare presented.",
publisher = "FIG (International Federation of Surveyors), Delft",
journal = "Proceedings of the 6th International FIG 3D Cadastre Workshop",
title = "Developing Serbian 3D Cadastre System - Challenges and Directions",
pages = "406-383",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1642"
}

Višnjevac, N., Mihajlović, R., Šoškić, M., Cvijetinović, Ž., Marošan, S.,& Bajat, B.. (2018). Developing Serbian 3D Cadastre System - Challenges and Directions. in Proceedings of the 6th International FIG 3D Cadastre Workshop
FIG (International Federation of Surveyors), Delft., 383-406.
https://hdl.handle.net/21.15107/rcub_grafar_1642

Višnjevac N, Mihajlović R, Šoškić M, Cvijetinović Ž, Marošan S, Bajat B. Developing Serbian 3D Cadastre System - Challenges and Directions. in Proceedings of the 6th International FIG 3D Cadastre Workshop. 2018;:383-406.
https://hdl.handle.net/21.15107/rcub_grafar_1642 .

Višnjevac, Nenad, Mihajlović, Rajica, Šoškić, Mladen, Cvijetinović, Željko, Marošan, Stevan, Bajat, Branislav, "Developing Serbian 3D Cadastre System - Challenges and Directions" in Proceedings of the 6th International FIG 3D Cadastre Workshop (2018):383-406,
https://hdl.handle.net/21.15107/rcub_grafar_1642 .

TY  - CHAP
AU  - Đurić, Uroš
AU  - Bajat, Branislav
AU  - Abolmasov, Biljana
AU  - Kovačević, Miloš
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1101
AB  - This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that include basic Machine Learning techniques (Artificial Neural Networks, Decision trees, Support Vector Machines) and additional useful procedures are described to show how they can be applied to address a complex problem such as landslide assessment. Two types of models are proposed in the work herein that are useful for describing landslide susceptibility and landslide prediction. The region of Halenkovice in Czech Republic is presented as a case study to illustrate and bring closer the practical aspects of landslide assessment. These aspects consider data preparation and preprocessing, scale effects, model optimization, and evaluation. The results show that Support Vector Machines and similar Machine Learning (ML) techniques can be successfully applied to address the zoning of landslide susceptibility, which might be an important breakthrough for potential applications in regional planning and decision-making.
PB  - Cham: Springer International Publishing
T2  - GeoComputational Analysis and Modeling of Regional Systems
T1  - Machine Learning and Landslide Assessment in a GIS Environment
EP  - 213
SP  - 191
DO  - 10.1007/978-3-319-59511-5_11
ER  - 

@inbook{
author = "Đurić, Uroš and Bajat, Branislav and Abolmasov, Biljana and Kovačević, Miloš",
year = "2018",
abstract = "This chapter introduces theoretical and practical aspects for applying GIS and geocomputation methods in landslide assessment problems. Machine Learning techniques in combination with GIS are proven useful for computation and building of complex non-linear spatial models, which is why they have been chosen in our work. Modeling principles that include basic Machine Learning techniques (Artificial Neural Networks, Decision trees, Support Vector Machines) and additional useful procedures are described to show how they can be applied to address a complex problem such as landslide assessment. Two types of models are proposed in the work herein that are useful for describing landslide susceptibility and landslide prediction. The region of Halenkovice in Czech Republic is presented as a case study to illustrate and bring closer the practical aspects of landslide assessment. These aspects consider data preparation and preprocessing, scale effects, model optimization, and evaluation. The results show that Support Vector Machines and similar Machine Learning (ML) techniques can be successfully applied to address the zoning of landslide susceptibility, which might be an important breakthrough for potential applications in regional planning and decision-making.",
publisher = "Cham: Springer International Publishing",
journal = "GeoComputational Analysis and Modeling of Regional Systems",
booktitle = "Machine Learning and Landslide Assessment in a GIS Environment",
pages = "213-191",
doi = "10.1007/978-3-319-59511-5_11"
}

Đurić, U., Bajat, B., Abolmasov, B.,& Kovačević, M.. (2018). Machine Learning and Landslide Assessment in a GIS Environment. in GeoComputational Analysis and Modeling of Regional Systems
Cham: Springer International Publishing., 191-213.
https://doi.org/10.1007/978-3-319-59511-5_11

Đurić U, Bajat B, Abolmasov B, Kovačević M. Machine Learning and Landslide Assessment in a GIS Environment. in GeoComputational Analysis and Modeling of Regional Systems. 2018;:191-213.
doi:10.1007/978-3-319-59511-5_11 .

Đurić, Uroš, Bajat, Branislav, Abolmasov, Biljana, Kovačević, Miloš, "Machine Learning and Landslide Assessment in a GIS Environment" in GeoComputational Analysis and Modeling of Regional Systems (2018):191-213,
https://doi.org/10.1007/978-3-319-59511-5_11 . .

TY  - CHAP
AU  - Bajat, Branislav
AU  - Kilibarda, Milan
AU  - Pejović, Milutin
AU  - Samardžić-Petrović, Mileva
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1106
PB  - Berlin Heidelberg: Springer
T2  - Spatial Analysis and Location Modeling in Urban and Regional Systems
T1  - Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps
EP  - 122
SP  - 97
DO  - 10.1007/978-3-642-37896-6_5
ER  - 

@inbook{
author = "Bajat, Branislav and Kilibarda, Milan and Pejović, Milutin and Samardžić-Petrović, Mileva",
year = "2018",
publisher = "Berlin Heidelberg: Springer",
journal = "Spatial Analysis and Location Modeling in Urban and Regional Systems",
booktitle = "Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps",
pages = "122-97",
doi = "10.1007/978-3-642-37896-6_5"
}

Bajat, B., Kilibarda, M., Pejović, M.,& Samardžić-Petrović, M.. (2018). Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps. in Spatial Analysis and Location Modeling in Urban and Regional Systems
Berlin Heidelberg: Springer., 97-122.
https://doi.org/10.1007/978-3-642-37896-6_5

Bajat B, Kilibarda M, Pejović M, Samardžić-Petrović M. Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps. in Spatial Analysis and Location Modeling in Urban and Regional Systems. 2018;:97-122.
doi:10.1007/978-3-642-37896-6_5 .

Bajat, Branislav, Kilibarda, Milan, Pejović, Milutin, Samardžić-Petrović, Mileva, "Spatial Hedonic Modeling of Housing Prices Using Auxiliary Maps" in Spatial Analysis and Location Modeling in Urban and Regional Systems (2018):97-122,
https://doi.org/10.1007/978-3-642-37896-6_5 . .

TY  - JOUR
AU  - Pejović, Milutin
AU  - Nikolić, Mladen
AU  - Heuvelink, Gerard B. M.
AU  - Hengl, Tomislav
AU  - Kilibarda, Milan
AU  - Bajat, Branislav
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/943
AB  - An approach for using lasso (Least Absolute Shrinkage and Selection Operator) regression in creating sparse 3D models of soil properties for spatial prediction at multiple depths is presented. Modeling soil properties in 3D benefits from interactions of spatial predictors with soil depth and its polynomial expansion, which yields a large number of model variables (and corresponding model parameters). Lasso is able to perform variable selection, hence reducing the number of model parameters and making the model more easily interpretable. This also prevents overfitting, which makes the model more accurate. The presented approach was tested using four variable selection approaches - none, stepwise, lasso and hierarchical lasso, on four kinds of models - standard linear model, linear model with polynomial expansion of depth, linear model with interactions of covariates with depth and linear model with interactions of covariates with depth and its polynomial expansion. This framework was used to predict Soil Organic Carbon (SOC) in three contrasting study areas: Bor (Serbia), Edgeroi (Australia) and the Netherlands. Results show that lasso yields substantial improvements in accuracy over standard and stepwise regression - up to 50 % of total variance. It yields models which contain up to five times less nonzero parameters than the full models and that are usually more sparse than models obtained by stepwise regression, up to three times. Extension of the standard linear model by including interactions typically improves the accuracy of models produced by lasso, but is detrimental to standard and stepwise regression. Regarding computation time, it was demonstrated that lasso is several orders of magnitude more efficient than stepwise regression for models with tens or hundreds of variables (including interactions). Proper model evaluation is emphasized. Considering the fact that lasso requires meta-parameter tuning, standard cross-validation does not suffice for adequate model evaluation, hence a nested cross-validation was employed. The presented approach is implemented as publicly available sparsereg3D R package.
PB  - Elsevier Ltd
T2  - Computers & Geosciences
T1  - Sparse regression interaction models for spatial prediction of soil properties in 3D
EP  - 13
SP  - 1
VL  - 118
DO  - 10.1016/j.cageo.2018.05.008
ER  - 

@article{
author = "Pejović, Milutin and Nikolić, Mladen and Heuvelink, Gerard B. M. and Hengl, Tomislav and Kilibarda, Milan and Bajat, Branislav",
year = "2018",
abstract = "An approach for using lasso (Least Absolute Shrinkage and Selection Operator) regression in creating sparse 3D models of soil properties for spatial prediction at multiple depths is presented. Modeling soil properties in 3D benefits from interactions of spatial predictors with soil depth and its polynomial expansion, which yields a large number of model variables (and corresponding model parameters). Lasso is able to perform variable selection, hence reducing the number of model parameters and making the model more easily interpretable. This also prevents overfitting, which makes the model more accurate. The presented approach was tested using four variable selection approaches - none, stepwise, lasso and hierarchical lasso, on four kinds of models - standard linear model, linear model with polynomial expansion of depth, linear model with interactions of covariates with depth and linear model with interactions of covariates with depth and its polynomial expansion. This framework was used to predict Soil Organic Carbon (SOC) in three contrasting study areas: Bor (Serbia), Edgeroi (Australia) and the Netherlands. Results show that lasso yields substantial improvements in accuracy over standard and stepwise regression - up to 50 % of total variance. It yields models which contain up to five times less nonzero parameters than the full models and that are usually more sparse than models obtained by stepwise regression, up to three times. Extension of the standard linear model by including interactions typically improves the accuracy of models produced by lasso, but is detrimental to standard and stepwise regression. Regarding computation time, it was demonstrated that lasso is several orders of magnitude more efficient than stepwise regression for models with tens or hundreds of variables (including interactions). Proper model evaluation is emphasized. Considering the fact that lasso requires meta-parameter tuning, standard cross-validation does not suffice for adequate model evaluation, hence a nested cross-validation was employed. The presented approach is implemented as publicly available sparsereg3D R package.",
publisher = "Elsevier Ltd",
journal = "Computers & Geosciences",
title = "Sparse regression interaction models for spatial prediction of soil properties in 3D",
pages = "13-1",
volume = "118",
doi = "10.1016/j.cageo.2018.05.008"
}

Pejović, M., Nikolić, M., Heuvelink, G. B. M., Hengl, T., Kilibarda, M.,& Bajat, B.. (2018). Sparse regression interaction models for spatial prediction of soil properties in 3D. in Computers & Geosciences
Elsevier Ltd., 118, 1-13.
https://doi.org/10.1016/j.cageo.2018.05.008

Pejović M, Nikolić M, Heuvelink GBM, Hengl T, Kilibarda M, Bajat B. Sparse regression interaction models for spatial prediction of soil properties in 3D. in Computers & Geosciences. 2018;118:1-13.
doi:10.1016/j.cageo.2018.05.008 .

Pejović, Milutin, Nikolić, Mladen, Heuvelink, Gerard B. M., Hengl, Tomislav, Kilibarda, Milan, Bajat, Branislav, "Sparse regression interaction models for spatial prediction of soil properties in 3D" in Computers & Geosciences, 118 (2018):1-13,
https://doi.org/10.1016/j.cageo.2018.05.008 . .

TY  - JOUR
AU  - Ceh, Marjan
AU  - Kilibarda, Milan
AU  - Lisec, Anka
AU  - Bajat, Branislav
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/959
AB  - The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008-2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R-2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.
PB  - MDPI AG
T2  - Isprs International Journal of Geo-Information
T1  - Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments
IS  - 5
VL  - 7
DO  - 10.3390/ijgi7050168
ER  - 

@article{
author = "Ceh, Marjan and Kilibarda, Milan and Lisec, Anka and Bajat, Branislav",
year = "2018",
abstract = "The goal of this study is to analyse the predictive performance of the random forest machine learning technique in comparison to commonly used hedonic models based on multiple regression for the prediction of apartment prices. A data set that includes 7407 records of apartment transactions referring to real estate sales from 2008-2013 in the city of Ljubljana, the capital of Slovenia, was used in order to test and compare the predictive performances of both models. Apparent challenges faced during modelling included (1) the non-linear nature of the prediction assignment task; (2) input data being based on transactions occurring over a period of great price changes in Ljubljana whereby a 28% decline was noted in six consecutive testing years; and (3) the complex urban form of the case study area. Available explanatory variables, organised as a Geographic Information Systems (GIS) ready dataset, including the structural and age characteristics of the apartments as well as environmental and neighbourhood information were considered in the modelling procedure. All performance measures (R-2 values, sales ratios, mean average percentage error (MAPE), coefficient of dispersion (COD)) revealed significantly better results for predictions obtained by the random forest method, which confirms the prospective of this machine learning technique on apartment price prediction.",
publisher = "MDPI AG",
journal = "Isprs International Journal of Geo-Information",
title = "Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments",
number = "5",
volume = "7",
doi = "10.3390/ijgi7050168"
}

Ceh, M., Kilibarda, M., Lisec, A.,& Bajat, B.. (2018). Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments. in Isprs International Journal of Geo-Information
MDPI AG., 7(5).
https://doi.org/10.3390/ijgi7050168

Ceh M, Kilibarda M, Lisec A, Bajat B. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments. in Isprs International Journal of Geo-Information. 2018;7(5).
doi:10.3390/ijgi7050168 .

Ceh, Marjan, Kilibarda, Milan, Lisec, Anka, Bajat, Branislav, "Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments" in Isprs International Journal of Geo-Information, 7, no. 5 (2018),
https://doi.org/10.3390/ijgi7050168 . .

TY  - JOUR
AU  - Višnjevac, Nenad
AU  - Mihajlović, Rajica
AU  - Šoškić, Mladen
AU  - Cvijetinović, Željko
AU  - Bajat, Branislav
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/868
AB  - The 3D cadastre concept brings data models, which are more complex than traditional 2D cadastral data models and could be followed by a large amount of data. The 3D cadastral data should be stored in database management systems, since the cadastral data integrity and consistency have to be satisfied. Relational database management system requires a tabular structure where data are stored within predefined columns and data types, and this could be uncomfortable for 3D cadastre until relational provides full 3D support. This study examines the possibility of using NoSQL databases in the 3D cadastre domain. NoSQL database stores unstructured data, which means that it is not required to define in advance what data types and categories will be used. From the 3D cadastre point of view, the NoSQL approach provides flexibility in data types and allows easier implementation of the 3D cadastral models. The implementation is conducted by using MongoDB and 3D Cadastral Data Model, where 3D cadastral data, including both alphanumerical and geometry part of data, are prepared for importing and then stored, managed, queried, and updated within NoSQL database. Furthermore, data stored in MongoDB are visualized and queried inside a web browser by using Cesium library.
PB  - Zvava Geodetov Slovenije
T2  - Geodetski vestnik
T1  - Using nosql databases in the 3d cadastre domain
EP  - 426
IS  - 3
SP  - 412
VL  - 61
DO  - 10.15292//geodetski-vestnik.2017.03.412-426
ER  - 

@article{
author = "Višnjevac, Nenad and Mihajlović, Rajica and Šoškić, Mladen and Cvijetinović, Željko and Bajat, Branislav",
year = "2017",
abstract = "The 3D cadastre concept brings data models, which are more complex than traditional 2D cadastral data models and could be followed by a large amount of data. The 3D cadastral data should be stored in database management systems, since the cadastral data integrity and consistency have to be satisfied. Relational database management system requires a tabular structure where data are stored within predefined columns and data types, and this could be uncomfortable for 3D cadastre until relational provides full 3D support. This study examines the possibility of using NoSQL databases in the 3D cadastre domain. NoSQL database stores unstructured data, which means that it is not required to define in advance what data types and categories will be used. From the 3D cadastre point of view, the NoSQL approach provides flexibility in data types and allows easier implementation of the 3D cadastral models. The implementation is conducted by using MongoDB and 3D Cadastral Data Model, where 3D cadastral data, including both alphanumerical and geometry part of data, are prepared for importing and then stored, managed, queried, and updated within NoSQL database. Furthermore, data stored in MongoDB are visualized and queried inside a web browser by using Cesium library.",
publisher = "Zvava Geodetov Slovenije",
journal = "Geodetski vestnik",
title = "Using nosql databases in the 3d cadastre domain",
pages = "426-412",
number = "3",
volume = "61",
doi = "10.15292//geodetski-vestnik.2017.03.412-426"
}

Višnjevac, N., Mihajlović, R., Šoškić, M., Cvijetinović, Ž.,& Bajat, B.. (2017). Using nosql databases in the 3d cadastre domain. in Geodetski vestnik
Zvava Geodetov Slovenije., 61(3), 412-426.
https://doi.org/10.15292//geodetski-vestnik.2017.03.412-426

Višnjevac N, Mihajlović R, Šoškić M, Cvijetinović Ž, Bajat B. Using nosql databases in the 3d cadastre domain. in Geodetski vestnik. 2017;61(3):412-426.
doi:10.15292//geodetski-vestnik.2017.03.412-426 .

Višnjevac, Nenad, Mihajlović, Rajica, Šoškić, Mladen, Cvijetinović, Željko, Bajat, Branislav, "Using nosql databases in the 3d cadastre domain" in Geodetski vestnik, 61, no. 3 (2017):412-426,
https://doi.org/10.15292//geodetski-vestnik.2017.03.412-426 . .

TY  - JOUR
AU  - Pejović, Milutin
AU  - Bajat, Branislav
AU  - Gospavić, Zagorka
AU  - Saljnikov, Elmira
AU  - Kilibarda, Milan
AU  - Cakmak, Dragan
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/821
AB  - Prevailing climatic conditions and local topography can be classified as the most influential environmental factors that affect the spatial dispersion of pollutants emanating from industrial sources. In this study, the combined effects of these factors were considered with respect to terrain exposure in order to explain the complex, spatial trend of Arsenic (As) concentration that was atmospherically-deposited from one of the largest Copper Mining and Smelting Complexes in Europe, Bor in Serbia. Several exposure parameters were created and employed as spatial covariates within the so-called "Spline-Then-Krige" approach for producing maps of As concentration at three standard soil depth layers (0-5 cm, 5-15 cm and 15-30 cm). The exposure parameters were created to quantify two different aspects of terrain exposure: Geometrical (Proximity) and Topographical exposure. Regression analysis confirmed the presence of a significant statistical association between the As data and all exposure parameters. The trend model showed good overall accuracy explaining 52% of the variance in As data for the surface soil layer, 49% for the middle layer and 35% for the deepest layer. Relative importance analysis revealed the importance of considering a more general model that includes interactions between exposure parameters. The kriging interpolation improved, to some extent, the regression accuracy for all three layers with R-2 values ranging from 55% for the surface layer to the 36% for the deepest soil layer. The prediction maps show that As contamination levels are well above allowable Serbian agricultural concentration limits (As lt mg/kg) for approximately 78% of the mapping area, thereby indicating that long term smelting activity leaves significant consequences on soil even on deeper unexposed layers.
PB  - Elsevier B.V.
T2  - Journal of Geochemical Exploration
T1  - Layer-specific spatial prediction of As concentration in copper smelter vicinity considering the terrain exposure
EP  - 35
SP  - 25
VL  - 179
DO  - 10.1016/j.gexplo.2017.05.004
ER  - 

@article{
author = "Pejović, Milutin and Bajat, Branislav and Gospavić, Zagorka and Saljnikov, Elmira and Kilibarda, Milan and Cakmak, Dragan",
year = "2017",
abstract = "Prevailing climatic conditions and local topography can be classified as the most influential environmental factors that affect the spatial dispersion of pollutants emanating from industrial sources. In this study, the combined effects of these factors were considered with respect to terrain exposure in order to explain the complex, spatial trend of Arsenic (As) concentration that was atmospherically-deposited from one of the largest Copper Mining and Smelting Complexes in Europe, Bor in Serbia. Several exposure parameters were created and employed as spatial covariates within the so-called "Spline-Then-Krige" approach for producing maps of As concentration at three standard soil depth layers (0-5 cm, 5-15 cm and 15-30 cm). The exposure parameters were created to quantify two different aspects of terrain exposure: Geometrical (Proximity) and Topographical exposure. Regression analysis confirmed the presence of a significant statistical association between the As data and all exposure parameters. The trend model showed good overall accuracy explaining 52% of the variance in As data for the surface soil layer, 49% for the middle layer and 35% for the deepest layer. Relative importance analysis revealed the importance of considering a more general model that includes interactions between exposure parameters. The kriging interpolation improved, to some extent, the regression accuracy for all three layers with R-2 values ranging from 55% for the surface layer to the 36% for the deepest soil layer. The prediction maps show that As contamination levels are well above allowable Serbian agricultural concentration limits (As lt mg/kg) for approximately 78% of the mapping area, thereby indicating that long term smelting activity leaves significant consequences on soil even on deeper unexposed layers.",
publisher = "Elsevier B.V.",
journal = "Journal of Geochemical Exploration",
title = "Layer-specific spatial prediction of As concentration in copper smelter vicinity considering the terrain exposure",
pages = "35-25",
volume = "179",
doi = "10.1016/j.gexplo.2017.05.004"
}

Pejović, M., Bajat, B., Gospavić, Z., Saljnikov, E., Kilibarda, M.,& Cakmak, D.. (2017). Layer-specific spatial prediction of As concentration in copper smelter vicinity considering the terrain exposure. in Journal of Geochemical Exploration
Elsevier B.V.., 179, 25-35.
https://doi.org/10.1016/j.gexplo.2017.05.004

Pejović M, Bajat B, Gospavić Z, Saljnikov E, Kilibarda M, Cakmak D. Layer-specific spatial prediction of As concentration in copper smelter vicinity considering the terrain exposure. in Journal of Geochemical Exploration. 2017;179:25-35.
doi:10.1016/j.gexplo.2017.05.004 .

Pejović, Milutin, Bajat, Branislav, Gospavić, Zagorka, Saljnikov, Elmira, Kilibarda, Milan, Cakmak, Dragan, "Layer-specific spatial prediction of As concentration in copper smelter vicinity considering the terrain exposure" in Journal of Geochemical Exploration, 179 (2017):25-35,
https://doi.org/10.1016/j.gexplo.2017.05.004 . .

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
AU  - Dragićević, Suzana
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/865
AB  - The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.
PB  - MDPI AG
T2  - Isprs International Journal of Geo-Information
T1  - Machine Learning Techniques for Modelling Short Term Land-Use Change
IS  - 12
VL  - 6
DO  - 10.3390/ijgi6120387
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Kovačević, Miloš and Bajat, Branislav and Dragićević, Suzana",
year = "2017",
abstract = "The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.",
publisher = "MDPI AG",
journal = "Isprs International Journal of Geo-Information",
title = "Machine Learning Techniques for Modelling Short Term Land-Use Change",
number = "12",
volume = "6",
doi = "10.3390/ijgi6120387"
}

Samardžić-Petrović, M., Kovačević, M., Bajat, B.,& Dragićević, S.. (2017). Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information
MDPI AG., 6(12).
https://doi.org/10.3390/ijgi6120387

Samardžić-Petrović M, Kovačević M, Bajat B, Dragićević S. Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information. 2017;6(12).
doi:10.3390/ijgi6120387 .

Samardžić-Petrović, Mileva, Kovačević, Miloš, Bajat, Branislav, Dragićević, Suzana, "Machine Learning Techniques for Modelling Short Term Land-Use Change" in Isprs International Journal of Geo-Information, 6, no. 12 (2017),
https://doi.org/10.3390/ijgi6120387 . .

TY  - CONF
AU  - Tošić, Nikola
AU  - Marinković, Snežana
AU  - Ignjatović, Ivan
AU  - Bajat, Branislav
AU  - Pejović, Milutin
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/829
AB  - The behavior of reinforced concrete under long-term loading is an important topic when designing concrete structures. Creep and shrinkage of concrete and long-term deflections of concrete structures, mainly reinforced concrete beams, have been studied for many decades but because of the long-term nature of these tests, existing experiments are relatively scarce. More importantly, there are significant challenges when carrying out these tests. These include achieving optimal beam design to ensure a realistic stress distribution with a developed cracked state, measuring deflections from self-weight, adequately measuring elastic deflections and strains, and instantaneously applying the imposed load. The problem is exacerbated with the advent of sustainable structural concretes containing recycled and waste materials-green concretes; they possess specific properties and experimental results for these concretes are even scarcer. In this paper, an experimental setup, designed to test the structural behavior of reinforced concrete beams in a four-point bending test under long-term loading, is described. Six full-scale beams were produced from natural aggregate concrete, recycled aggregate concrete and high-volume fly ash concrete-two beams from each concrete type, one loaded after 7 and the other after 28 days. The load consisted of beam self-weight and imposed permanent load. The beam design procedure is explained, which enabled an adequate stress distribution and crack formation with a minimum imposed load. A method was developed to use geodetic equipment and measurements to determine the deflection from self-weight and a special support and loading structure were designed to enable instant imposed load application and the measurement of elastic deflections and strains. The complete test setup, equipment and procedure are explained and the advantages and deficiencies of this approach are discussed.
PB  - Springer International Publishing
C3  - High Tech Concrete: Where Technology and Engineering Meet - Proceedings of the 2017 fib Symposium
T1  - Experimental setup for measuring long-term behavior of green reinforced concrete beams
EP  - 2364
SP  - 2356
DO  - 10.1007/978-3-319-59471-2_268
ER  - 

@conference{
author = "Tošić, Nikola and Marinković, Snežana and Ignjatović, Ivan and Bajat, Branislav and Pejović, Milutin",
year = "2017",
abstract = "The behavior of reinforced concrete under long-term loading is an important topic when designing concrete structures. Creep and shrinkage of concrete and long-term deflections of concrete structures, mainly reinforced concrete beams, have been studied for many decades but because of the long-term nature of these tests, existing experiments are relatively scarce. More importantly, there are significant challenges when carrying out these tests. These include achieving optimal beam design to ensure a realistic stress distribution with a developed cracked state, measuring deflections from self-weight, adequately measuring elastic deflections and strains, and instantaneously applying the imposed load. The problem is exacerbated with the advent of sustainable structural concretes containing recycled and waste materials-green concretes; they possess specific properties and experimental results for these concretes are even scarcer. In this paper, an experimental setup, designed to test the structural behavior of reinforced concrete beams in a four-point bending test under long-term loading, is described. Six full-scale beams were produced from natural aggregate concrete, recycled aggregate concrete and high-volume fly ash concrete-two beams from each concrete type, one loaded after 7 and the other after 28 days. The load consisted of beam self-weight and imposed permanent load. The beam design procedure is explained, which enabled an adequate stress distribution and crack formation with a minimum imposed load. A method was developed to use geodetic equipment and measurements to determine the deflection from self-weight and a special support and loading structure were designed to enable instant imposed load application and the measurement of elastic deflections and strains. The complete test setup, equipment and procedure are explained and the advantages and deficiencies of this approach are discussed.",
publisher = "Springer International Publishing",
journal = "High Tech Concrete: Where Technology and Engineering Meet - Proceedings of the 2017 fib Symposium",
title = "Experimental setup for measuring long-term behavior of green reinforced concrete beams",
pages = "2364-2356",
doi = "10.1007/978-3-319-59471-2_268"
}

Tošić, N., Marinković, S., Ignjatović, I., Bajat, B.,& Pejović, M.. (2017). Experimental setup for measuring long-term behavior of green reinforced concrete beams. in High Tech Concrete: Where Technology and Engineering Meet - Proceedings of the 2017 fib Symposium
Springer International Publishing., 2356-2364.
https://doi.org/10.1007/978-3-319-59471-2_268

Tošić N, Marinković S, Ignjatović I, Bajat B, Pejović M. Experimental setup for measuring long-term behavior of green reinforced concrete beams. in High Tech Concrete: Where Technology and Engineering Meet - Proceedings of the 2017 fib Symposium. 2017;:2356-2364.
doi:10.1007/978-3-319-59471-2_268 .

Tošić, Nikola, Marinković, Snežana, Ignjatović, Ivan, Bajat, Branislav, Pejović, Milutin, "Experimental setup for measuring long-term behavior of green reinforced concrete beams" in High Tech Concrete: Where Technology and Engineering Meet - Proceedings of the 2017 fib Symposium (2017):2356-2364,
https://doi.org/10.1007/978-3-319-59471-2_268 . .

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Dragićević, Suzana
AU  - Kovačević, Miloš
AU  - Bajat, Branislav
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/777
AB  - Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.
PB  - Blackwell Publishing Ltd
T2  - Transactions in Gis
T1  - Modeling Urban Land Use Changes Using Support Vector Machines
EP  - 734
IS  - 5
SP  - 718
VL  - 20
DO  - 10.1111/tgis.12174
ER  - 

@article{
author = "Samardžić-Petrović, Mileva and Dragićević, Suzana and Kovačević, Miloš and Bajat, Branislav",
year = "2016",
abstract = "Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation-based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM-based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.",
publisher = "Blackwell Publishing Ltd",
journal = "Transactions in Gis",
title = "Modeling Urban Land Use Changes Using Support Vector Machines",
pages = "734-718",
number = "5",
volume = "20",
doi = "10.1111/tgis.12174"
}

Samardžić-Petrović, M., Dragićević, S., Kovačević, M.,& Bajat, B.. (2016). Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis
Blackwell Publishing Ltd., 20(5), 718-734.
https://doi.org/10.1111/tgis.12174

Samardžić-Petrović M, Dragićević S, Kovačević M, Bajat B. Modeling Urban Land Use Changes Using Support Vector Machines. in Transactions in Gis. 2016;20(5):718-734.
doi:10.1111/tgis.12174 .

Samardžić-Petrović, Mileva, Dragićević, Suzana, Kovačević, Miloš, Bajat, Branislav, "Modeling Urban Land Use Changes Using Support Vector Machines" in Transactions in Gis, 20, no. 5 (2016):718-734,
https://doi.org/10.1111/tgis.12174 . .