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The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.
Details
; Arabameri, Alireza 2 ; Saha, Asish 1
; Rabin Chakrabortty 1
; Blaschke, Thomas 3
; Pradhan, Biswajeet 4
; Band, Shahab S 5
1 Department of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, India;
2 Department of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, Iran;
3 Department of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, Austria;
4 Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo NSW 2007, Australia;
5 Future Technology Research Center, College of Future, National Yunlin University of Science and 21 Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan