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 5 / 34 are safeguarded [24,40–43]. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data are used for spatial analysis. The gradient of the terrain or slope influences hydrological and geomorphological processes [44]. The coastal slope influences surface runoff, sediment transport, and stability, and on the Senegal coast, gentle slopes are more susceptible to erosion due to inadequate water drainage and surface water accumulation, whereas steeper slopes may mitigate wave impact but heighten the risk of landslides [24,42,43,45]. The slope layer was generated from the DEM. Vertical Distance from Channel Network (VDCN) quantifies elevation of a location with adjacent drainage channels [46]. It indicates the accumulation of surface water and fluvial activity, where VDCN values that are lower show a higher risk of erosion from overland flow and waterlogging, and those that are increasing indicate a reduced risk [43]. DEM made it possible to derive VDCN. The Topographic Wetness Index (TWI) evaluates local slope and upstream contributing area in order to measure soil texture distribution [47]. High TWI values indicate water accumulation and saturation, which can compromise soil stability and enhance susceptibility to erosion [43]. Conversely, low TWI values depict dry, more stable conditions. The DEM was utilized to derive the TWI via hydrological modelling. The Topographic Position Index (TPI)

quantifies the height of a point above or below the average height of its surroundings [48]. This index can be utilized to classify hills, valleys, and plains. Positive values of TPI express high landforms with lower susceptibility to erosion, whereas negative values of TPI signify depressions and valleys with high water retention capacity [43,49]. The TPI layer was developed based on the 30-meter resolution DEM data. The Topographic Ruggedness Index (TRI) quantifies height variation in a region to evaluate terrain heterogeneity [50]. Cliffs and dunes have elevated TRI values, which can either exacerbate or mitigate erosion based on substrate hardness where Rugged terrain with pliable substrates may erode more rapidly, whereas rocky regions may exhibit greater resistance [43,51]. TRI was derived from DEM. The flow of water Table 1. Data sources of parameters for mitigating coastal. Category Indicators Data Sources Data Type Unit Resolution Period Topographical Aspect Derived from DEM Raster – 30m 2024 Curvature Derived from DEM Raster – 30m 2024 Digital Elevation Model SRTM Raster Meter 30m 2024 Distance to Coastline Author Vector Degrees (°) – 2024 Slope Derived from DEM Raster Degrees (°) 30m 2024 Topographic Position Index Derived from DEM Raster – 30m 2024 Topographic Ruggedness Index Derived from DEM Raster – 30m 2024 Hydrological Distance to River Humanitarian Datasets Vector Degrees (°) – 2024 Mean Sea Level Permanent Service for Mean Sea Level (PSMSL) Vector Meter – –

Topographic Wetness Index Derived from DEM Raster – 30m 2024 Vertical Distance from Channel Network Derived from DEM Raster – 30m 2024 Meteorological Aridity Index WorldClim Raster – 30m 2024 Mean Wind Speed ERA5 Raster km/h 30m 2024 Precipitation CHIPS Raster mm 30m 2024 Geological Distance to Faultline Author Vector Degrees (°) – 2025 Geology USGS Earth Explorer Vector – – 2024 Geomorphology Geological Survey of Bangladesh Raster – 30m 2024 Soil Texture FAO Global Soil Map Vector – – 2024 Land Use & Human Activity Land Use and Land Cover Sentinal-2 Image Raster – 10m 2024 Normalized Difference Vegetation Index Sentinal-2 Image Raster – 10m 2024 https://doi.org/10.1371/journal.pone.0334347.t001