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 24 / 34 probably because of things like higher elevation, more vegetation, and a more inland location. Many districts, such as Barguna (91.45%), and Feni (90.13%), are dominated by moderate-risk zones. The spatial distribution of erosion suscep- tibility resembles well with [142,143] who also recognized Patuakhali and Cox’s Bazar as areas for vulnerability. Although their research focused on tidal-driven riverbank erosion, the current findings point to a combined effect of low elevation, vegetation loss, and weather stresses. Anthropogenic and climatic factors, which are frequently missing from determinis- tic models like GloSEM and RUSLE, allow for this wider viewpoint. This makes the model a more complete tool for both current and future coastal erosion risk assessment. This research is distinct from previous studies that it combines CMIP5-based climate scenarios. The risk of coastal ero- sion is predicted to significantly increase under future climate scenarios, especially under high-emission pathways. High- risk areas are expected to rise from 26% in 2040 to a peak of 50% by 2080, then slightly fall to 40% by 2100, according to RCP 8.5. RCP 6.0 shows similar patterns, with high-risk zones reaching a peak of 48% by 2080. Peak high-risk area coverage, on the other hand, rises more moderately at RCP 2.6 and 4.5, reaching 47% and 45%, respectively. These forecasts highlight how severe coastal damage could occur in the event of unchecked climate change. These patterns

Fig 13. District-wise coastal erosion Susceptibility zones by the best model (XGBoost). https://doi.org/10.1371/journal.pone.0334347.g013