Estimation of Urban Area Change in Eskişehir Province Using Remote Sensing Data and Machine Learning Algorithms

Abstract

Rapid population growth, natural events, and increasing industrialization are among the factors affecting land use. To keep this change under control and to make sound plans, it is necessary to control the changes. In this study, the spatial use change in the Eskişehir region between the years 1990-2018 was examined with CORINE data. Based on this determined change, an urban change model was created with the multivariate regression method. As a result of the evaluations, while an increase was observed in urban areas and pastures between 1990-2018, a decrease was determined in agricultural and forest areas. This change is defined as 43.74% in urban areas, 3.28% in agricultural areas, 7.78% in forest areas, and 60.10% in pasture areas. SMOReg, MLP Regressor, and M5P Model Tree methods were used for the estimation study to be carried out with the obtained spatial change data. Urban values for 2018 were estimated to find the best method. Finally, the areas of 2030 were estimated with the method that gave the best results. The results demonstrated the usability of modeling using CORINE data.