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Open AccessFeature PaperArticle

Machine Learning Techniques for Modelling Short Term Land-Use Change

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Faculty of Civil Engineering, University of Belgrade, Bulevar kralja Aleksandra 73, 11000 Belgrade, Serbia
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Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada
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Authors to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(12), 387; https://doi.org/10.3390/ijgi6120387
Received: 31 October 2017 / Revised: 24 November 2017 / Accepted: 26 November 2017 / Published: 29 November 2017
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. View Full-Text
Keywords: land use change; spatial modelling; machine learning; neural networks; Decision Trees; Support Vector Machines land use change; spatial modelling; machine learning; neural networks; Decision Trees; Support Vector Machines
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Samardžić-Petrović, M.; Kovačević, M.; Bajat, B.; Dragićević, S. Machine Learning Techniques for Modelling Short Term Land-Use Change. ISPRS Int. J. Geo-Inf. 2017, 6, 387.

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