Maqola

Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment

Indrajit ChowdhuriDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaSubodh Chandra PalDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaAlireza ArabameriDepartment of Geomorphology, Tarbiat Modares University, Tehran 14117-13116, IranAsish SahaDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaRabin ChakraborttyDepartment of Geography, The University of Burdwan, Bardhaman, West Bengal 713104, IndiaThomas BlaschkeDepartment of Geoinformatics–Z_GIS, University of Salzburg, 5020 Salzburg, AustriaBiswajeet PradhanCenter of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi ArabiaShahab. S. BandFuture Technology Research Center, College of Future, National Yunlin University of Science and 21 Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

2020en

ABI

Annotatsiya

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.

Hali tarjima qilinmagan

Identifikatorlar

DOI: 10.3390/rs12213620

Iqtiboslar va manbalar

2 ta iqtibos0 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar