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

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

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

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

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