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.
Highlights
Studying, understanding, and modeling the land use change (LUC) process is important and represents one of the key research topic for many disciplines, geography, urban planning, geo-information science, ecology, and land use science [1,2,3]
The results indicate that all three machine learning (ML) techniques can be used effectively for short-term forecasting of LUC, but the Support Vector Machines (SVM) achieved the highest agreement of predicted changes
This research examined LUC models that were based on ML techniques in an urban environment with nine land use classes
Summary
Studying, understanding, and modeling the land use change (LUC) process is important and represents one of the key research topic for many disciplines, geography, urban planning, geo-information science, ecology, and land use science [1,2,3]. The main focus of data-driven modeling methods is to find patterns and trends or to induce representative models of underlying processes using past data [16,17]. These modeling methods assume stationarity in the relationship between the predictors and land change variables [18]. It is possible to discover the relationship between process inputs and outputs without the need for detailed understanding of the physical transformation process or transition functions
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