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 17 / 34 found in the middle and northern sections. In terms of soil texture(d), silt, loam, and clay soils are prevalent in the center region, whilst brown/gray soils are predominant in the eastern section. Description of land use & human activity factors In Fig 8, according to LULC(a) map built-up areas are mostly found in the eastern and northwestern regions, whereas vegetation predominates in the southwestern regions. High NDVI(b) values are observed in the eastern and northeastern regions, characterized by extensive vegetation. Moderate NDVI values are allocated between vegetation and urban areas. Independent variable selection Table 3 displays the results of the multicollinearity evaluation and statistical significance testing employed to identify the most impactful factors for the model. Parameters having p-values exceeding 0.05, including average curvature, DtR, LULC, TPI, and TWI, were excluded and denoted with a double star (**). MSL and Precipitation parameters were excluded because of high VIF values, denoted with triple star (***) (>10). The correlation matrix for every variable is shown in Fig 9. The pair MSL and DtF in this matrix exhibited a strong positive correlation (r = 0.89), surpassing the 0.80 threshold for multicollinearity. Consequently, MSL was eliminated from that pair because of its high correlation and excessively high VIF value. Notably, the Aridity Index exhibits a positive correlation with precipitation (r = 0.77), whereas

precipitation shows a strong negative correlation with both DtF (r = −0.95) and MSL (r = −0.87). There were also moderate correlations found between DEM and Geology (r = 0.52) and between Slope and DEM (r = 0.77). Fig 10 indicates that NDVI is the predominant factor in AdaBoost, CatBoost, GBDT, LightGBM, XGBoost, Bagging, DT, RF and TreeBag, with scores of 19, 22, 23, 16, 26, 22, 20, 28 and 24, respectively. Nonetheless, with a value of 20 the Aridity Index is the predominant factor in the avNNet model. The scores of 14, 14, 12, 16, 18 and 14 for the Aridity Index have a significant impact on AdaBoost, GBDT, LightGBM, Bagging, RF, and TreeBag. With scores of 14, 15 and 15 in CatBoost, XGBoost and Decision Trees, respectively, DtC also has a significant influence. Significantly, excluding avNNet, Geology and Soil Texture demonstrate the lowest significance across all models. Fig 8. Land use & Human activity factors (a) LULC, (b) NDVI. https://doi.org/10.1371/journal.pone.0334347.g008