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 6 / 34 over landforms is influenced by the curvature of the ground [52]. Concave landforms retain water and silt, heightening erosion risk, whereas convex parts facilitate drainage and diminish moisture retention [24,43,53]. The curvature layer was generated using GIS Curvature with DEM data. Precipitation and potential evapotranspiration dictate climate aridity in the Aridity Index [54]. An increased aridity index indicates a drier environment with diminished vegetation, rendering land more Susceptibility to wind and water erosion, whereas low-aridity regions possess abundant flora, which stabilizes the soil [43,55]. This index was developed using WorldClim datasets. Microclimatic elements such as wind patterns and sunlight exposure are influenced by slope aspect [56]. Coastal windward slopes are more susceptible to wave and wind forces, heightening the risk of erosion, whereas leeward slopes are protected [43]. DEM data yielded aspect values. The Nor- malized Difference Vegetation Index (NDVI) is a widely utilized remote sensing statistic for assessing vegetation density and health [57,58]. Elevated NDVI values indicate abundant vegetation, enhancing soil structure and mitigating erosion, and low NDVI signifies diminished vegetation or bare soil, which is more susceptible to erosion [24,43]. NDVI data were obtained from Sentinel-2 imagery at a resolution of 10 meters. Precipitation influences the hydrological cycle and erosion,

where heavy rainfall enhances surface runoff and soil saturation, accelerating erosion [43,59]. CHIPS precipitation data were utilized in the study. The mean wind speed significantly influences coastal erosion by producing wave energy and transferring aeolian silt [60]. Wind velocity amplifies wave dynamics, resulting in coastal erosion and the inland transport of materials so calm circumstances diminish coastal erosion [43,61]. Wind speed data was used from ERA5 reanaly- sis datasets in order to examine these effects. Additionally, geomorphology examines landform classifications and their geological characteristics, which influence coastal erosion [62]. Rocky coastal formations such as cliffs and headlands exhibit superior erosion resistance compared to sandy beaches [42,63]. The geomorphological categories utilized in this study were gained from the Geological Survey of Bangladesh. Soil texture effect coastal erosion where Clay soils exhibit greater compaction and erosion resistance compared to sandy soils, which are readily moved by wind and water [42,64]. Soil texture data was obtained from the FAO Global Soil Map. The distance to the coastline quantifies the proximity of a location to the shore that influence coastal erosion [42]. Coastal regions are especially susceptible to wave dynamics, tidal influences, and saline intrusion, which intensify erosion [39]. Distance was ascertained via shape file data. The spatial distribution of anthropogenic and natural surface elements that influence coastal erosion, such as urban areas, forests,

and agricultural lands, is depicted by Land Use and Land Cover (LULC) statistics [15,24,42]. The research employs Esri’s Sentinel-2 Global Land Cover 2023 datasets at a 10-meter resolution, a globally recognized dataset derived from high-resolution satellite imagery for land use and land cover analysis [65,66]. The distance to a river indicates a location’s closeness to fluvial systems that provide water and sediment that affect coastal erosion, where it occurs more frequently in proximity to rivers than at greater distances [67]. The river distance layer employs humanitarian vector datasets. Fault lines can influence ground stability and long-term erosion, where active tectonic faults may experience subsidence or uplift, hence augmenting coastal instability and sediment redistribution [16]. Stable locations are typically situated distant from fault lines. Increasing mean sea level, a long-term average of sea surface elevation, induces coastal erosion [39]. Elevated sea levels exacerbate coastal erosion, saline intrusion, and storm surges. Stable or declining sea levels mitigate these effects. The Permanent Service for Mean Sea Level utilized MSL data. The erosion resistance of an area is contin- gent upon its geological materials. Basalt and granite exhibit durability, but shale and sedimentary formations are more susceptible to erosion under hydrodynamic forces [68]. From the USGS Earth Explorer, utilized geological data. Erosion inventory mapping Fig 2 illustrates the coastal erosion map, demonstrating the spatial distribution of soil erosion intensity throughout the

research area. This map was produced utilizing the Revised Universal Soil Loss Equation (RUSLE) model, which quan- tifies yearly soil loss in tons per hectare per year. The Revised Universal Soil Loss Equation (RUSLE) was developed by Renard [69] and is widely used and popular all over the world for average annual soil loss estimation. [70]. In this research, the model was used to analyze the potential soil erosion in the coastal zone of Bangladesh. As [71], said, the