Investigation of land-subsidence phenomenon and aquifer vulnerability using machine models and GIS technique

Adel Ghasemi (), Omid Bahmani (), Samira Akhavan () and Hamid Reza Pourghasemi ()
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Adel Ghasemi: Bu-Ali Sina University
Omid Bahmani: Bu-Ali Sina University
Samira Akhavan: Bu-Ali Sina University
Hamid Reza Pourghasemi: Shiraz University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 118, issue 2, No 35, 1645-1671

Abstract: Abstract 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 profile curvatures, slope aspect, Topographic Wetness Index (TWI), Normalized Difference 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 efficiency than the SVM model. To determine each parameter's effect 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 effect 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 confirmed a decrease in groundwater level, which has led to land-subsidence in the aquifer.

Keywords: Land-subsidence; Machine learning; Random forest; Support vector machine; PLS; GRACE satellite (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06058-y

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