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Abstract
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In this study, a land-subsidence vulnerability map has been prepared using Machine Learning (ML) models fusing, Random Forest (RF), Support Vector Machine (SVM) and a GIS technique in the Hamadan Province, Iran. The information layers used as input of the ML models in “R” software are comprised of elevation, slope, plan and profle curvatures, slope aspect, Topographic Wetness Index (TWI), Normalized Diference Vegetation Index (NDVI), soil texture, distance from rivers, distance from the fault, geology, land use, and groundwater level drawdown. The accuracy of the results obtained stood at 89% in the SVM, and up to 96% in the RF models. The RF model demonstrates a greater efciency than the SVM model. To determine each parameter’s efect on the land-subsidence of the study area, the Partial Least Squares (PLS) model has been used in the R software. The use of the PLS model shows a greater efect of elevation and groundwater level decline compared to the other parameters on the land-subsidence phenomenon. Finally, the raster vulnerability map in the GIS software was divided into four classes in terms of intensity as ‘low,’ ‘medium,’ ‘high,’ and ‘very high’ utilizing the natural break method. In the optimal RF model 45% of aquifers were assessed as being low, 23% as moderate, 20% as high, and 12% as very high. The study of the groundwater changing process, using GRACE satellite data in Google Earth Engine environment confrmed a decrease in groundwater level, which has led to land-subsidence in the aquifer.
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