TY  - JOUR
AU  - Gojković, Zoran
AU  - Kilibarda, Milan
AU  - Brajović, Ljiljana
AU  - Marjanović, Miloš
AU  - Milutinović, Aleksandar
AU  - Ganić, Aleksandar
PY  - 2023
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3458
AB  - Open pit coal mining affects surrounding populated areas, resulting in terrain surface deformation. Surface deformation should be monitored as often as possible to control deformations and prevent potential incidents. This paper analyzes time series deformation estimated from the Sentinel-1 satellite images using the Persistent Scatterer Interferometry method to monitor subsidence rates caused by open pit mining activities. It is possible to measure deformations using classical geodetic methods, but those are rarely used in practice because they are time-consuming and expensive for application in large areas. Using the open access radar images from the Sentinel-1 mission, 513 images from the repository were downloaded between October 2016 and the end of December 2020. We present the processing steps in detail in order to establish a workflow for the automated processing of vertical displacement estimation using open source tools; a total of 402 images were processed: 215 images belonged to the ascending satellite orbit, 187 images belonged to the descending orbit, and 111 images were rejected because of adverse weather conditions. The PS InSAR technique has never been used for the mines of the Republic of Serbia or for land surveying practices related to deformation monitoring. The results based on the Sentinel-1 images were compared with results from geodetic leveling and with neotectonic uplift trends. The trend lines of vertical displacement obtained from PS and corresponding leveling are significantly similar (a Pearson correlation of 85% with a p-value of 0.015). The final evaluation reported results of vertical displacements at the leveling benchmark of −3.4 mm/year with the PS InSAR method and −2.7 mm/year with the leveling method. A comparison of the PS vertical displacements with a settlement model fits reasonably, suggesting that the measurements are valid. As four years of PS time series data is insufficient to establish undisputable conclusions on the neotectonics uplift, extending the time series (covering at least a decade) implies that this approach will become attractive in future neotectonic uplift trend estimations. This study illustrates not only the ability of Sentinel-1 data in mapping vertical deformations, but the obtained results could also be used for geohazard monitoring and land monitoring in general for the area of interest.
PB  - MDPI
T2  - Remote Sensing
T1  - Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine
IS  - 10
SP  - 2519
VL  - 15
DO  - 10.3390/rs15102519
ER  - 

@article{
author = "Gojković, Zoran and Kilibarda, Milan and Brajović, Ljiljana and Marjanović, Miloš and Milutinović, Aleksandar and Ganić, Aleksandar",
year = "2023",
abstract = "Open pit coal mining affects surrounding populated areas, resulting in terrain surface deformation. Surface deformation should be monitored as often as possible to control deformations and prevent potential incidents. This paper analyzes time series deformation estimated from the Sentinel-1 satellite images using the Persistent Scatterer Interferometry method to monitor subsidence rates caused by open pit mining activities. It is possible to measure deformations using classical geodetic methods, but those are rarely used in practice because they are time-consuming and expensive for application in large areas. Using the open access radar images from the Sentinel-1 mission, 513 images from the repository were downloaded between October 2016 and the end of December 2020. We present the processing steps in detail in order to establish a workflow for the automated processing of vertical displacement estimation using open source tools; a total of 402 images were processed: 215 images belonged to the ascending satellite orbit, 187 images belonged to the descending orbit, and 111 images were rejected because of adverse weather conditions. The PS InSAR technique has never been used for the mines of the Republic of Serbia or for land surveying practices related to deformation monitoring. The results based on the Sentinel-1 images were compared with results from geodetic leveling and with neotectonic uplift trends. The trend lines of vertical displacement obtained from PS and corresponding leveling are significantly similar (a Pearson correlation of 85% with a p-value of 0.015). The final evaluation reported results of vertical displacements at the leveling benchmark of −3.4 mm/year with the PS InSAR method and −2.7 mm/year with the leveling method. A comparison of the PS vertical displacements with a settlement model fits reasonably, suggesting that the measurements are valid. As four years of PS time series data is insufficient to establish undisputable conclusions on the neotectonics uplift, extending the time series (covering at least a decade) implies that this approach will become attractive in future neotectonic uplift trend estimations. This study illustrates not only the ability of Sentinel-1 data in mapping vertical deformations, but the obtained results could also be used for geohazard monitoring and land monitoring in general for the area of interest.",
publisher = "MDPI",
journal = "Remote Sensing",
title = "Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine",
number = "10",
pages = "2519",
volume = "15",
doi = "10.3390/rs15102519"
}

Gojković, Z., Kilibarda, M., Brajović, L., Marjanović, M., Milutinović, A.,& Ganić, A.. (2023). Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine. in Remote Sensing
MDPI., 15(10), 2519.
https://doi.org/10.3390/rs15102519

Gojković Z, Kilibarda M, Brajović L, Marjanović M, Milutinović A, Ganić A. Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine. in Remote Sensing. 2023;15(10):2519.
doi:10.3390/rs15102519 .

Gojković, Zoran, Kilibarda, Milan, Brajović, Ljiljana, Marjanović, Miloš, Milutinović, Aleksandar, Ganić, Aleksandar, "Ground Surface Subsidence Monitoring Using Sentinel-1 in the “Kostolac” Open Pit Coal Mine" in Remote Sensing, 15, no. 10 (2023):2519,
https://doi.org/10.3390/rs15102519 . .

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  - Witjes, Martijn
AU  - Parente, Leandro
AU  - J van Diemen, Chris
AU  - Hengl, Tomislav
AU  - Landa, Martin
AU  - Brodský, Lukáš
AU  - Halounova, Lena
AU  - Križan, Josip
AU  - Antonić, Luka
AU  - Ilie, Codrina Maria
AU  - Craciunescu, Vasile
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/3343
AB  - A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.
T2  - PeerJ
T1  - A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat
IS  - 10
SP  - e13573
DO  - doi.org/10.7717/peerj.13573
ER  - 

@article{
author = "Witjes, Martijn and Parente, Leandro and J van Diemen, Chris and Hengl, Tomislav and Landa, Martin and Brodský, Lukáš and Halounova, Lena and Križan, Josip and Antonić, Luka and Ilie, Codrina Maria and Craciunescu, Vasile and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka",
year = "2022",
abstract = "A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python.",
journal = "PeerJ",
title = "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat",
number = "10",
pages = "e13573",
doi = "doi.org/10.7717/peerj.13573"
}

Witjes, M., Parente, L., J van Diemen, C., Hengl, T., Landa, M., Brodský, L., Halounova, L., Križan, J., Antonić, L., Ilie, C. M., Craciunescu, V., Kilibarda, M., Antonijević, O.,& Glušica, L.. (2022). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ(10), e13573.
https://doi.org/doi.org/10.7717/peerj.13573

Witjes M, Parente L, J van Diemen C, Hengl T, Landa M, Brodský L, Halounova L, Križan J, Antonić L, Ilie CM, Craciunescu V, Kilibarda M, Antonijević O, Glušica L. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. in PeerJ. 2022;(10):e13573.
doi:doi.org/10.7717/peerj.13573 .

Witjes, Martijn, Parente, Leandro, J van Diemen, Chris, Hengl, Tomislav, Landa, Martin, Brodský, Lukáš, Halounova, Lena, Križan, Josip, Antonić, Luka, Ilie, Codrina Maria, Craciunescu, Vasile, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, "A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat" in PeerJ, no. 10 (2022):e13573,
https://doi.org/doi.org/10.7717/peerj.13573 . .

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  - Hengl, Tomislav
AU  - Miller, Matthew A. E.
AU  - Križan, Josip
AU  - Shepherd, Keith D.
AU  - Sila, Andrew
AU  - Kilibarda, Milan
AU  - Antonijević, Ognjen
AU  - Glušica, Luka
AU  - Dobermann, Achim
AU  - Haefele, Stephan M.
AU  - McGrath, Steve P.
AU  - Acquah, Gifty E.
AU  - Collinson, Jamie
AU  - Parente, Leandro
AU  - Sheykhmousa, Mohammadreza
AU  - Saito, Kazuki
AU  - Johnson, Jean‑Martial
AU  - Chamberlin, Jordan
AU  - Silatsa, Francis B. T.
AU  - Yemefack, Martin
AU  - Wendt, John
AU  - MacMillan, Robert A.
AU  - Wheeler, Ichsani
AU  - Crouch, Jonathan
PY  - 2021
UR  - https://www.nature.com/articles/s41598-021-85639-y
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2362
AB  - Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.
T2  - Scientific Reports
T1  - African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning
IS  - 11
SP  - 6130 (2021)
DO  - https://doi.org/10.1038/s41598-021-85639-y
ER  - 

@article{
author = "Hengl, Tomislav and Miller, Matthew A. E. and Križan, Josip and Shepherd, Keith D. and Sila, Andrew and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka and Dobermann, Achim and Haefele, Stephan M. and McGrath, Steve P. and Acquah, Gifty E. and Collinson, Jamie and Parente, Leandro and Sheykhmousa, Mohammadreza and Saito, Kazuki and Johnson, Jean‑Martial and Chamberlin, Jordan and Silatsa, Francis B. T. and Yemefack, Martin and Wendt, John and MacMillan, Robert A. and Wheeler, Ichsani and Crouch, Jonathan",
year = "2021",
abstract = "Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N≈150,000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.",
journal = "Scientific Reports",
title = "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning",
number = "11",
pages = "6130 (2021)",
doi = "https://doi.org/10.1038/s41598-021-85639-y"
}

Hengl, T., Miller, M. A. E., Križan, J., Shepherd, K. D., Sila, A., Kilibarda, M., Antonijević, O., Glušica, L., Dobermann, A., Haefele, S. M., McGrath, S. P., Acquah, G. E., Collinson, J., Parente, L., Sheykhmousa, M., Saito, K., Johnson, J., Chamberlin, J., Silatsa, F. B. T., Yemefack, M., Wendt, J., MacMillan, R. A., Wheeler, I.,& Crouch, J.. (2021). African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports(11), 6130 (2021).
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y

Hengl T, Miller MAE, Križan J, Shepherd KD, Sila A, Kilibarda M, Antonijević O, Glušica L, Dobermann A, Haefele SM, McGrath SP, Acquah GE, Collinson J, Parente L, Sheykhmousa M, Saito K, Johnson J, Chamberlin J, Silatsa FBT, Yemefack M, Wendt J, MacMillan RA, Wheeler I, Crouch J. African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning. in Scientific Reports. 2021;(11):6130 (2021).
doi:https://doi.org/10.1038/s41598-021-85639-y .

Hengl, Tomislav, Miller, Matthew A. E., Križan, Josip, Shepherd, Keith D., Sila, Andrew, Kilibarda, Milan, Antonijević, Ognjen, Glušica, Luka, Dobermann, Achim, Haefele, Stephan M., McGrath, Steve P., Acquah, Gifty E., Collinson, Jamie, Parente, Leandro, Sheykhmousa, Mohammadreza, Saito, Kazuki, Johnson, Jean‑Martial, Chamberlin, Jordan, Silatsa, Francis B. T., Yemefack, Martin, Wendt, John, MacMillan, Robert A., Wheeler, Ichsani, Crouch, Jonathan, "African soil properties and nutrients mapped at 30 m spatial resolution using two‑scale ensemble machine learning" in Scientific Reports, no. 11 (2021):6130 (2021),
https://doi.org/https://doi.org/10.1038/s41598-021-85639-y . .

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  - Nestorov, Ivan
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
PY  - 2020
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2088
AB  - There is an increasing need for pan-European spatial datasets, mainly to support the common European Union policies. This has inevitably raised demands for adopting pan-European cartographic projections to visualize the spatial data. The Map Projection Workshop organized by EuroGeographics in 2001 provided a recommendation to the European Commission to adopt the Lambert conformal conic projection for conformal pan-European mapping at the scales smaller or equal to 1:500,000. This paper discusses if the projection is an optimal solution in terms of linear deformations over the mapping region. An optimized CAMPREL (conformal adaptive mapping projection of rotation ellipsoid) projection for the area of interest is proposed as an alternative solution. The projection quality criteria were calculated and compared with those of the Lambert conformal conic projection. The maximal possible absolute linear distortion for conformal mapping of the pan-European area is also given. It has been shown that the CAMPREL projection designed for pan-European mapping better meets the projection selection criteria.
PB  - Association of Surveyors of Slovenia
T2  - Geodetski Vestnik
T1  - THE OPTIMAL CONFORMAL PROJECTION FOR PAN-EUROPEAN MAPPING
IS  - 2
VL  - 64
DO  - DOI: 10.15292/geodetski-vestnik.2020.02.214-226
ER  - 

@article{
author = "Nestorov, Ivan and Kilibarda, Milan and Protić, Dragutin",
year = "2020",
abstract = "There is an increasing need for pan-European spatial datasets, mainly to support the common European Union policies. This has inevitably raised demands for adopting pan-European cartographic projections to visualize the spatial data. The Map Projection Workshop organized by EuroGeographics in 2001 provided a recommendation to the European Commission to adopt the Lambert conformal conic projection for conformal pan-European mapping at the scales smaller or equal to 1:500,000. This paper discusses if the projection is an optimal solution in terms of linear deformations over the mapping region. An optimized CAMPREL (conformal adaptive mapping projection of rotation ellipsoid) projection for the area of interest is proposed as an alternative solution. The projection quality criteria were calculated and compared with those of the Lambert conformal conic projection. The maximal possible absolute linear distortion for conformal mapping of the pan-European area is also given. It has been shown that the CAMPREL projection designed for pan-European mapping better meets the projection selection criteria.",
publisher = "Association of Surveyors of Slovenia",
journal = "Geodetski Vestnik",
title = "THE OPTIMAL CONFORMAL PROJECTION FOR PAN-EUROPEAN MAPPING",
number = "2",
volume = "64",
doi = "DOI: 10.15292/geodetski-vestnik.2020.02.214-226"
}

Nestorov, I., Kilibarda, M.,& Protić, D.. (2020). THE OPTIMAL CONFORMAL PROJECTION FOR PAN-EUROPEAN MAPPING. in Geodetski Vestnik
Association of Surveyors of Slovenia., 64(2).
https://doi.org/DOI: 10.15292/geodetski-vestnik.2020.02.214-226

Nestorov I, Kilibarda M, Protić D. THE OPTIMAL CONFORMAL PROJECTION FOR PAN-EUROPEAN MAPPING. in Geodetski Vestnik. 2020;64(2).
doi:DOI: 10.15292/geodetski-vestnik.2020.02.214-226 .

Nestorov, Ivan, Kilibarda, Milan, Protić, Dragutin, "THE OPTIMAL CONFORMAL PROJECTION FOR PAN-EUROPEAN MAPPING" in Geodetski Vestnik, 64, no. 2 (2020),
https://doi.org/DOI: 10.15292/geodetski-vestnik.2020.02.214-226 . .

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  - CONF
AU  - Miljković, Stefan
AU  - Antonijević, Ognjen
AU  - Kilibarda, Milan
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1586
AB  - During the establishment of first order trigonometric network in Serbia, the influence of Earth’s gravitational field on measurements was not considered. As a consequence, all points have position errors. With additional measurements and calculations, these errors were later determined for X and Y coordinates of each point. This paper uses geostatistical methods (regression kriging) to estimate error prediction model for any location in Serbia.
PB  - Građevinski fakultet, Subotica
C3  - Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018
T1  - Prediction of position errors of points in first order trigonometric network
T1  - Predikcija položajne greške tačaka trigonometrijske mreže prvog reda
EP  - 725
SP  - 717
VL  - 34
DO  - 10.14415/konferencijaGFS2018.070
ER  - 

@conference{
author = "Miljković, Stefan and Antonijević, Ognjen and Kilibarda, Milan",
year = "2018",
abstract = "During the establishment of first order trigonometric network in Serbia, the influence of Earth’s gravitational field on measurements was not considered. As a consequence, all points have position errors. With additional measurements and calculations, these errors were later determined for X and Y coordinates of each point. This paper uses geostatistical methods (regression kriging) to estimate error prediction model for any location in Serbia.",
publisher = "Građevinski fakultet, Subotica",
journal = "Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018",
title = "Prediction of position errors of points in first order trigonometric network, Predikcija položajne greške tačaka trigonometrijske mreže prvog reda",
pages = "725-717",
volume = "34",
doi = "10.14415/konferencijaGFS2018.070"
}

Miljković, S., Antonijević, O.,& Kilibarda, M.. (2018). Prediction of position errors of points in first order trigonometric network. in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018
Građevinski fakultet, Subotica., 34, 717-725.
https://doi.org/10.14415/konferencijaGFS2018.070

Miljković S, Antonijević O, Kilibarda M. Prediction of position errors of points in first order trigonometric network. in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018. 2018;34:717-725.
doi:10.14415/konferencijaGFS2018.070 .

Miljković, Stefan, Antonijević, Ognjen, Kilibarda, Milan, "Prediction of position errors of points in first order trigonometric network" in Zbornik radova 6. međunarodne konferencije Savremena dostignuća u građevinarstvu 2018, 34 (2018):717-725,
https://doi.org/10.14415/konferencijaGFS2018.070 . .

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  - Protić, Dragutin
AU  - Kilibarda, Milan
AU  - Nenkovic-Riznić, Marina D.
AU  - Nestorov, Ivan
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/960
AB  - Solar maps as web cartographic products that provide information on solar potential of surfaces on the Earth have been exploited in decision making, awareness raising, and promoting the use of solar energy. Web based solar maps of cities have become popular services as the use of solar energy is especially attractive in urban environments. The article discusses the concept and aspects of urban solar potential maps on the example of the i-Scope project as a case study. The i-Scope roof solar potential service built on 3-D urban information models was piloted in eight European cities. To obtain precise data on solar irradiation, a good quality digital surface model is required. A cost efficient innovative method for generation of digital surface model from stereophotogrammetry for urban areas where no advanced source data (e. g. LiDAR) exist is developed. The method works for flat, shed and gable roofs and provides sufficient accuracy of digital surface model.
PB  - Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd
T2  - Thermal Science
T1  - Three-dimensional urban solar potential maps case study of the i-scope project
EP  - 673
IS  - 1
SP  - 663
VL  - 22
DO  - 10.2298/TSCI170715213P
ER  - 

@article{
author = "Protić, Dragutin and Kilibarda, Milan and Nenkovic-Riznić, Marina D. and Nestorov, Ivan",
year = "2018",
abstract = "Solar maps as web cartographic products that provide information on solar potential of surfaces on the Earth have been exploited in decision making, awareness raising, and promoting the use of solar energy. Web based solar maps of cities have become popular services as the use of solar energy is especially attractive in urban environments. The article discusses the concept and aspects of urban solar potential maps on the example of the i-Scope project as a case study. The i-Scope roof solar potential service built on 3-D urban information models was piloted in eight European cities. To obtain precise data on solar irradiation, a good quality digital surface model is required. A cost efficient innovative method for generation of digital surface model from stereophotogrammetry for urban areas where no advanced source data (e. g. LiDAR) exist is developed. The method works for flat, shed and gable roofs and provides sufficient accuracy of digital surface model.",
publisher = "Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd",
journal = "Thermal Science",
title = "Three-dimensional urban solar potential maps case study of the i-scope project",
pages = "673-663",
number = "1",
volume = "22",
doi = "10.2298/TSCI170715213P"
}

Protić, D., Kilibarda, M., Nenkovic-Riznić, M. D.,& Nestorov, I.. (2018). Three-dimensional urban solar potential maps case study of the i-scope project. in Thermal Science
Univerzitet u Beogradu - Institut za nuklearne nauke Vinča, Beograd., 22(1), 663-673.
https://doi.org/10.2298/TSCI170715213P

Protić D, Kilibarda M, Nenkovic-Riznić MD, Nestorov I. Three-dimensional urban solar potential maps case study of the i-scope project. in Thermal Science. 2018;22(1):663-673.
doi:10.2298/TSCI170715213P .

Protić, Dragutin, Kilibarda, Milan, Nenkovic-Riznić, Marina D., Nestorov, Ivan, "Three-dimensional urban solar potential maps case study of the i-scope project" in Thermal Science, 22, no. 1 (2018):663-673,
https://doi.org/10.2298/TSCI170715213P . .

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  - Tadić-Percec, Melita
AU  - Kilibarda, Milan
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/954
PB  - Geofizicki Zavod
T2  - Geofizika
T1  - Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"
EP  - 224
IS  - 2
SP  - 223
VL  - 34
UR  - https://hdl.handle.net/21.15107/rcub_grafar_954
ER  - 

@article{
author = "Tadić-Percec, Melita and Kilibarda, Milan",
year = "2018",
publisher = "Geofizicki Zavod",
journal = "Geofizika",
title = "Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"",
pages = "224-223",
number = "2",
volume = "34",
url = "https://hdl.handle.net/21.15107/rcub_grafar_954"
}

Tadić-Percec, M.,& Kilibarda, M.. (2018). Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences". in Geofizika
Geofizicki Zavod., 34(2), 223-224.
https://hdl.handle.net/21.15107/rcub_grafar_954

Tadić-Percec M, Kilibarda M. Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences". in Geofizika. 2018;34(2):223-224.
https://hdl.handle.net/21.15107/rcub_grafar_954 .

Tadić-Percec, Melita, Kilibarda, Milan, "Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"" in Geofizika, 34, no. 2 (2018):223-224,
https://hdl.handle.net/21.15107/rcub_grafar_954 .

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  - Hengl, Tomislav
AU  - de Jesus, Jorge Mendes
AU  - Heuvelink, Gerard B. M.
AU  - Gonzalez, Maria Ruiperez
AU  - Kilibarda, Milan
AU  - Blagotić, Aleksandar
AU  - Shangguan, Wei
AU  - Wright, Marvin N.
AU  - Geng, Xiaoyuan
AU  - Bauer-Marschallinger, Bernhard
AU  - Guevara, Mario Antonio
AU  - Vargas, Rodrigo
AU  - MacMillan, Robert A.
AU  - Batjes, Niels H.
AU  - Leenaars, Johan G. B.
AU  - Ribeiro, Eloi
AU  - Wheeler, Ichsani
AU  - Mantel, Stephan
AU  - Kempen, Bas
PY  - 2017
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/885
AB  - This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods D random forest and gradient boosting and/or multinomial logistic regression D as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10 -fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
PB  - Public Library of Science
T2  - PLOS One
T1  - SoilGrids250m: Global gridded soil information based on machine learning
IS  - 2
VL  - 12
DO  - 10.1371/journal.pone.0169748
ER  - 

@article{
author = "Hengl, Tomislav and de Jesus, Jorge Mendes and Heuvelink, Gerard B. M. and Gonzalez, Maria Ruiperez and Kilibarda, Milan and Blagotić, Aleksandar and Shangguan, Wei and Wright, Marvin N. and Geng, Xiaoyuan and Bauer-Marschallinger, Bernhard and Guevara, Mario Antonio and Vargas, Rodrigo and MacMillan, Robert A. and Batjes, Niels H. and Leenaars, Johan G. B. and Ribeiro, Eloi and Wheeler, Ichsani and Mantel, Stephan and Kempen, Bas",
year = "2017",
abstract = "This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods D random forest and gradient boosting and/or multinomial logistic regression D as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10 -fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.",
publisher = "Public Library of Science",
journal = "PLOS One",
title = "SoilGrids250m: Global gridded soil information based on machine learning",
number = "2",
volume = "12",
doi = "10.1371/journal.pone.0169748"
}

Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Gonzalez, M. R., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S.,& Kempen, B.. (2017). SoilGrids250m: Global gridded soil information based on machine learning. in PLOS One
Public Library of Science., 12(2).
https://doi.org/10.1371/journal.pone.0169748

Hengl T, de Jesus JM, Heuvelink GBM, Gonzalez MR, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B, Guevara MA, Vargas R, MacMillan RA, Batjes NH, Leenaars JGB, Ribeiro E, Wheeler I, Mantel S, Kempen B. SoilGrids250m: Global gridded soil information based on machine learning. in PLOS One. 2017;12(2).
doi:10.1371/journal.pone.0169748 .

Hengl, Tomislav, de Jesus, Jorge Mendes, Heuvelink, Gerard B. M., Gonzalez, Maria Ruiperez, Kilibarda, Milan, Blagotić, Aleksandar, Shangguan, Wei, Wright, Marvin N., Geng, Xiaoyuan, Bauer-Marschallinger, Bernhard, Guevara, Mario Antonio, Vargas, Rodrigo, MacMillan, Robert A., Batjes, Niels H., Leenaars, Johan G. B., Ribeiro, Eloi, Wheeler, Ichsani, Mantel, Stephan, Kempen, Bas, "SoilGrids250m: Global gridded soil information based on machine learning" in PLOS One, 12, no. 2 (2017),
https://doi.org/10.1371/journal.pone.0169748 . .

TY  - CONF
AU  - Protić, Dragutin
AU  - Milutinović, S.
AU  - Antonijević, Ognjen
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1427
PB  - Faculty of Civil Engineering, Belgrade
C3  - Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
T1  - Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1427
ER  - 

@conference{
author = "Protić, Dragutin and Milutinović, S. and Antonijević, Ognjen and Sekulić, Aleksandar and Kilibarda, Milan",
year = "2016",
publisher = "Faculty of Civil Engineering, Belgrade",
journal = "Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016",
title = "Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1427"
}

Protić, D., Milutinović, S., Antonijević, O., Sekulić, A.,& Kilibarda, M.. (2016). Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics. in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
Faculty of Civil Engineering, Belgrade..
https://hdl.handle.net/21.15107/rcub_grafar_1427

Protić D, Milutinović S, Antonijević O, Sekulić A, Kilibarda M. Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics. in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016. 2016;.
https://hdl.handle.net/21.15107/rcub_grafar_1427 .

Protić, Dragutin, Milutinović, S., Antonijević, Ognjen, Sekulić, Aleksandar, Kilibarda, Milan, "Sensitivity of vegetation indices derived from Sentinel-2 data to change in biophysical characteristics" in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016 (2016),
https://hdl.handle.net/21.15107/rcub_grafar_1427 .

TY  - CONF
AU  - Sekulić, Aleksandar
AU  - Pejović, Milutin
AU  - Kilibarda, Milan
AU  - Bajat, Branislav
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1494
PB  - Croatian Geodetic Society, Zagreb
C3  - SIG 2016 : proceedings of the International Symposium on Engineering Geodesy, Varaždin, May 20-22, 2016
T1  - Development of Interactive 1D/2D Geodetic Control Network Design and Adjustment Software in Open Source/Free Environment (R + Google Earth + Google Maps)
EP  - 223
SP  - 213
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1494
ER  - 

@conference{
author = "Sekulić, Aleksandar and Pejović, Milutin and Kilibarda, Milan and Bajat, Branislav",
year = "2016",
publisher = "Croatian Geodetic Society, Zagreb",
journal = "SIG 2016 : proceedings of the International Symposium on Engineering Geodesy, Varaždin, May 20-22, 2016",
title = "Development of Interactive 1D/2D Geodetic Control Network Design and Adjustment Software in Open Source/Free Environment (R + Google Earth + Google Maps)",
pages = "223-213",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1494"
}

Sekulić, A., Pejović, M., Kilibarda, M.,& Bajat, B.. (2016). Development of Interactive 1D/2D Geodetic Control Network Design and Adjustment Software in Open Source/Free Environment (R + Google Earth + Google Maps). in SIG 2016 : proceedings of the International Symposium on Engineering Geodesy, Varaždin, May 20-22, 2016
Croatian Geodetic Society, Zagreb., 213-223.
https://hdl.handle.net/21.15107/rcub_grafar_1494

Sekulić A, Pejović M, Kilibarda M, Bajat B. Development of Interactive 1D/2D Geodetic Control Network Design and Adjustment Software in Open Source/Free Environment (R + Google Earth + Google Maps). in SIG 2016 : proceedings of the International Symposium on Engineering Geodesy, Varaždin, May 20-22, 2016. 2016;:213-223.
https://hdl.handle.net/21.15107/rcub_grafar_1494 .

Sekulić, Aleksandar, Pejović, Milutin, Kilibarda, Milan, Bajat, Branislav, "Development of Interactive 1D/2D Geodetic Control Network Design and Adjustment Software in Open Source/Free Environment (R + Google Earth + Google Maps)" in SIG 2016 : proceedings of the International Symposium on Engineering Geodesy, Varaždin, May 20-22, 2016 (2016):213-223,
https://hdl.handle.net/21.15107/rcub_grafar_1494 .

TY  - CONF
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
PY  - 2016
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1497
PB  - Faculty of Civil Engineering, Belgrade
C3  - Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
T1  - High resolution daily temperature for Serbia (1960-2015)
EP  - 49
SP  - 47
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1497
ER  - 

@conference{
author = "Sekulić, Aleksandar and Kilibarda, Milan",
year = "2016",
publisher = "Faculty of Civil Engineering, Belgrade",
journal = "Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016",
title = "High resolution daily temperature for Serbia (1960-2015)",
pages = "49-47",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1497"
}

Sekulić, A.,& Kilibarda, M.. (2016). High resolution daily temperature for Serbia (1960-2015). in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016
Faculty of Civil Engineering, Belgrade., 47-49.
https://hdl.handle.net/21.15107/rcub_grafar_1497

Sekulić A, Kilibarda M. High resolution daily temperature for Serbia (1960-2015). in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016. 2016;:47-49.
https://hdl.handle.net/21.15107/rcub_grafar_1497 .

Sekulić, Aleksandar, Kilibarda, Milan, "High resolution daily temperature for Serbia (1960-2015)" in Proceedings of GeoMLA, Geostatistics and Machine Learning, Application in Climate and Environmental Sciences, Belgrade, Serbia 21-24 June 2016 (2016):47-49,
https://hdl.handle.net/21.15107/rcub_grafar_1497 .

TY  - JOUR
AU  - Bajat, Branislav
AU  - Blagojević, Dragan
AU  - Kilibarda, Milan
AU  - Luković, Jelena
AU  - Tosić, Ivana
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/721
AB  - The spatial analysis of annual and seasonal temperature trends in Serbia during the period 1961-2010 was carried out using mean monthly data from 64 meteorological stations. Change year detection was achieved using cumulative sum charts. The magnitude of trends was derived from the slopes of linear trends using the least square method. The same formalism of least square method was used to assess the statistical significance of the determined trends. Maps of temperature trends were generated by applying a spatial regression method to visualize the detected tendencies. The obtained results indicate a negative temperature trend for the period before the change year except for winter and a more pronounced positive trend after the change year. Besides being more pronounced, the vast majority of trends after the change year were also clearly statistically significant. Our estimate of the average temperature trend over Serbia is in agreement with those obtained at the global and European scale. Calculated global autocorrelation statistics (Moran's I) indicate an apparent random spatial pattern of temperature trends across the Serbia for both periods before and after the change year.
PB  - Springer-Verlag Wien
T2  - Theoretical and Applied Climatology
T1  - Spatial analysis of the temperature trends in Serbia during the period 1961-2010
EP  - 301
IS  - 1-2
SP  - 289
VL  - 121
DO  - 10.1007/s00704-014-1243-7
ER  - 

@article{
author = "Bajat, Branislav and Blagojević, Dragan and Kilibarda, Milan and Luković, Jelena and Tosić, Ivana",
year = "2015",
abstract = "The spatial analysis of annual and seasonal temperature trends in Serbia during the period 1961-2010 was carried out using mean monthly data from 64 meteorological stations. Change year detection was achieved using cumulative sum charts. The magnitude of trends was derived from the slopes of linear trends using the least square method. The same formalism of least square method was used to assess the statistical significance of the determined trends. Maps of temperature trends were generated by applying a spatial regression method to visualize the detected tendencies. The obtained results indicate a negative temperature trend for the period before the change year except for winter and a more pronounced positive trend after the change year. Besides being more pronounced, the vast majority of trends after the change year were also clearly statistically significant. Our estimate of the average temperature trend over Serbia is in agreement with those obtained at the global and European scale. Calculated global autocorrelation statistics (Moran's I) indicate an apparent random spatial pattern of temperature trends across the Serbia for both periods before and after the change year.",
publisher = "Springer-Verlag Wien",
journal = "Theoretical and Applied Climatology",
title = "Spatial analysis of the temperature trends in Serbia during the period 1961-2010",
pages = "301-289",
number = "1-2",
volume = "121",
doi = "10.1007/s00704-014-1243-7"
}

Bajat, B., Blagojević, D., Kilibarda, M., Luković, J.,& Tosić, I.. (2015). Spatial analysis of the temperature trends in Serbia during the period 1961-2010. in Theoretical and Applied Climatology
Springer-Verlag Wien., 121(1-2), 289-301.
https://doi.org/10.1007/s00704-014-1243-7

Bajat B, Blagojević D, Kilibarda M, Luković J, Tosić I. Spatial analysis of the temperature trends in Serbia during the period 1961-2010. in Theoretical and Applied Climatology. 2015;121(1-2):289-301.
doi:10.1007/s00704-014-1243-7 .

Bajat, Branislav, Blagojević, Dragan, Kilibarda, Milan, Luković, Jelena, Tosić, Ivana, "Spatial analysis of the temperature trends in Serbia during the period 1961-2010" in Theoretical and Applied Climatology, 121, no. 1-2 (2015):289-301,
https://doi.org/10.1007/s00704-014-1243-7 . .

TY  - JOUR
AU  - Luković, Jelena
AU  - Blagojević, Dragan
AU  - Kilibarda, Milan
AU  - Bajat, Branislav
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/726
AB  - This study examines the spatial pattern of relationships between annual, seasonal and monthly rainfall in Serbia, and the North Atlantic Oscillation (NAO) for the period of 1961-2009. The first correlation analysis between rainfall and the NAO was performed using a Pearson product-moment test. Results suggested negative, mainly statistically significant correlations at annual and winter scales as was expected. However, the highest percentage of stations showed significant result in October suggesting a strong impact of a large scale atmospheric mode throughout a wet season in Serbia. Further spatial analysis that incorporated a spatial autocorrelation statistic of correlation coefficients showed significant clustering at all temporal scales.
PB  - Elsevier
T2  - Spatial Statistics
T1  - Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia
EP  - 52
SP  - 39
VL  - 14
DO  - 10.1016/j.spasta.2015.04.007
ER  - 

@article{
author = "Luković, Jelena and Blagojević, Dragan and Kilibarda, Milan and Bajat, Branislav",
year = "2015",
abstract = "This study examines the spatial pattern of relationships between annual, seasonal and monthly rainfall in Serbia, and the North Atlantic Oscillation (NAO) for the period of 1961-2009. The first correlation analysis between rainfall and the NAO was performed using a Pearson product-moment test. Results suggested negative, mainly statistically significant correlations at annual and winter scales as was expected. However, the highest percentage of stations showed significant result in October suggesting a strong impact of a large scale atmospheric mode throughout a wet season in Serbia. Further spatial analysis that incorporated a spatial autocorrelation statistic of correlation coefficients showed significant clustering at all temporal scales.",
publisher = "Elsevier",
journal = "Spatial Statistics",
title = "Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia",
pages = "52-39",
volume = "14",
doi = "10.1016/j.spasta.2015.04.007"
}

Luković, J., Blagojević, D., Kilibarda, M.,& Bajat, B.. (2015). Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia. in Spatial Statistics
Elsevier., 14, 39-52.
https://doi.org/10.1016/j.spasta.2015.04.007

Luković J, Blagojević D, Kilibarda M, Bajat B. Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia. in Spatial Statistics. 2015;14:39-52.
doi:10.1016/j.spasta.2015.04.007 .

Luković, Jelena, Blagojević, Dragan, Kilibarda, Milan, Bajat, Branislav, "Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia" in Spatial Statistics, 14 (2015):39-52,
https://doi.org/10.1016/j.spasta.2015.04.007 . .