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

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