Machine Learning Techniques for Modelling Short Term Land-Use Change
Апстракт
The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM a...chieved the highest agreement of predicted changes.
Кључне речи:
land use change / spatial modelling / machine learning / neural networks / Decision Trees / Support Vector MachinesИзвор:
Isprs International Journal of Geo-Information, 2017, 6, 12Издавач:
- MDPI AG
Финансирање / пројекти:
- Улога и имплементација државног просторног плана и регионалних развојних докумената у обнови стратешког истраживања, мишљења и управљања у Србији (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-47014)
- Natural Sciences and Engineering Research Council (NSERC) of Canada
DOI: 10.3390/ijgi6120387
ISSN: 2220-9964
WoS: 000419217200009
Scopus: 2-s2.0-85044600950
Институција/група
GraFarTY - JOUR AU - Samardžić-Petrović, Mileva AU - Kovačević, Miloš AU - Bajat, Branislav AU - Dragićević, Suzana PY - 2017 UR - https://grafar.grf.bg.ac.rs/handle/123456789/865 AB - The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes. PB - MDPI AG T2 - Isprs International Journal of Geo-Information T1 - Machine Learning Techniques for Modelling Short Term Land-Use Change IS - 12 VL - 6 DO - 10.3390/ijgi6120387 ER -
@article{ author = "Samardžić-Petrović, Mileva and Kovačević, Miloš and Bajat, Branislav and Dragićević, Suzana", year = "2017", abstract = "The representation of land use change (LUC) is often achieved by using data-driven methods that include machine learning (ML) techniques. The main objectives of this research study are to implement three ML techniques, Decision Trees (DT), Neural Networks (NN), and Support Vector Machines (SVM) for LUC modeling, in order to compare these three ML techniques and to find the appropriate data representation. The ML techniques are applied on the case study of LUC in three municipalities of the City of Belgrade, the Republic of Serbia, using historical geospatial data sets and considering nine land use classes. The ML models were built and assessed using two different time intervals. The information gain ranking technique and the recursive attribute elimination procedure were implemented to find the most informative attributes that were related to LUC in the study area. The results indicate that all three ML techniques can be used effectively for short-term forecasting of LUC, but the SVM achieved the highest agreement of predicted changes.", publisher = "MDPI AG", journal = "Isprs International Journal of Geo-Information", title = "Machine Learning Techniques for Modelling Short Term Land-Use Change", number = "12", volume = "6", doi = "10.3390/ijgi6120387" }
Samardžić-Petrović, M., Kovačević, M., Bajat, B.,& Dragićević, S.. (2017). Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information MDPI AG., 6(12). https://doi.org/10.3390/ijgi6120387
Samardžić-Petrović M, Kovačević M, Bajat B, Dragićević S. Machine Learning Techniques for Modelling Short Term Land-Use Change. in Isprs International Journal of Geo-Information. 2017;6(12). doi:10.3390/ijgi6120387 .
Samardžić-Petrović, Mileva, Kovačević, Miloš, Bajat, Branislav, Dragićević, Suzana, "Machine Learning Techniques for Modelling Short Term Land-Use Change" in Isprs International Journal of Geo-Information, 6, no. 12 (2017), https://doi.org/10.3390/ijgi6120387 . .