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

Abstract

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.

Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

“…MC is defined as the linear relationship among the two or more variables (Alin 2010). Basically, in a regression model, MC analysis has been carried out when two or more variables are correlated among each other (Chowdhuri et al 2020;Chen and Li 2020). The result of traditional statistical method in feature selection procedure has ambiguity and affected by extreme values because of the presence of large dataset (Pal et al 2020;Chen, Li, et al 2021).…”

GIS and remote sensing-based assessment of soil erosion risk using RUSLE model in South-Kivu province, eastern, Democratic Republic of Congo

“…Consequently, threatens the stability and health of society, socioeconomic welfare and accentuates malnutrition Chowdhuri et al 2020;). As it is presently estimated that more than one billion persons, or one out of every seven people on the planet, is hungry and/or malnourished (Idso and Idso 2011), a problem likely to become extreme as the world population is estimating to reach 9.7 billion by 2050 (Lutz and K C, 2010).…”

Landslide-Induced Mass Transport of Radionuclides along Transboundary Mailuu-Suu River Networks in Central Asia

“…The translational landslide mass moves along a roughly planar surface with little rotation or backward tilting, and the seismically triggered translational landslides are likely to cause secondary losses associated with earthquakes, with effects sometimes exceeding those of direct shaking [10,11]. In case of a landslide that occurs in the areas of tailings ponds and dumps, the sudden release of radioactively contaminated sediments into the local surface waters encompasses both physical and chemical processes and may cause instantaneous hydraulic changes, redistribution of sediments, and an increase in suspended particulate matter (SPM) [12][13][14]. Due to the significant societal, economic, and environmental impacts related to landslide-induced mass movements, quantifying the mass underlying the subsequence of landslides has become key to prevent radioactive pollution through surface waters, particularly through river networks [13,15,16].…”

“…In case of a landslide that occurs in the areas of tailings ponds and dumps, the sudden release of radioactively contaminated sediments into the local surface waters encompasses both physical and chemical processes and may cause instantaneous hydraulic changes, redistribution of sediments, and an increase in suspended particulate matter (SPM) [12][13][14]. Due to the significant societal, economic, and environmental impacts related to landslide-induced mass movements, quantifying the mass underlying the subsequence of landslides has become key to prevent radioactive pollution through surface waters, particularly through river networks [13,15,16]. The methods for mass estimation can be basically classified into five types: field surveys, physically-based modeling, empirical modeling, multi-temporal digital elevation model (DEM) analysis, and geometrical estimation [17,18].…”