Predicting coastal erosion susceptibility in Bangladesh under climate scenario via machine learning techniques

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 7 / 34 RUSLE model is represented by the equation A = R × K × LS × C × P, where A is the potential yearly soil loss in tons per hectare per year, R is the rainfall-runoff erosivity factor in megajoules per hectare per hour per year, K is the soil erodibil- ity factor in tons per meter per hour, LS is the slope length and steepness factor, C is the cover and management factor, and P is the conservation support practice factor. CHIRPS (Climate Hazards Group InfraRed Precipitation with Station Data) daily precipitation records were utilized in the computation of the rainfall erosivity factor, R [72]. The K factor for soil erodibility was computed from data on several soil types where categorical reclassification was performed based on soil texture classes, and empirically estimated K values were allocated to each class [73]. The Topographic Factor (LS) is the compound effect of slope steepness and length, whereby the slope calculation was performed based on the Shuttle Radar Topography Mission (SRTM) data and the digital elevation model (DEM). Once the slope had been converted from degrees to percentages, the LS was then calculated once the conversion had been carried out. The Cover Management Factor (C) was derived by calculating the Normalized Difference Vegetation Index (NDVI) from the Landsat-8 photo sur- face reflectance bands [74]. A function of NDVI was applied to transform vegetation density to a C factor. This correction

normalizes the values of C to between 0 and 1 and implies that areas with denser plant cover are less susceptible to erosion. Slope data and MODIS land cover type (LC_Type1) were utilized in developing the Support Practice Factor (P) [75]. Diverse P values were assigned to the distinct land cover types and slope categories, grounded in the methodologies defined in the literature. The application of effective soil conservation techniques resulted in reduced P values on gentle Fig 2. Geospatial Distribution of Coastal Erosion. https://doi.org/10.1371/journal.pone.0334347.g002