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 26 / 34 Treebag, averaging Neural Network) and boosting (XGBoost, LightGBM, CatBoost, GBDT, AdaBoost). XGBoost was used to get an area under the curve (AUC) of 0.95, which means it was better at predicting the future and finding high- risk areas. The results show big differences in geography, with most of the high-risk places being in Bhola, Cox’s Bazar, and Patuakhali. This was the breakdown of high-risk places in these three areas: 19.41%, 26.20%, and 21.47%. Barguna (91.45%), and Feni (90.13%) have a relatively high number of places that are considered to have moderate risk. RCP models 2.6, 4.5, 6.0, and 8.5 all predict that the number of high-risk areas will grow in the years 2040, 2060, 2080, and 2100. This is especially true for RCP 8.5, which says that shares will grow by 50% by 2080 and by 40% by 2100. When the RCP number is 2.6, 4.5, or 6.0, it means that the risk has changed, though only slightly. These results show how important it is to build buildings that can withstand climate change, use methods to stop erosion, and make policies that adapt to new situations. It is important to protect especially Susceptibility groups by setting up map early warning systems, enforcing zoning laws, building marine embankments, and planting trees. Our results also point to the ethical challenge of Fig 14. From 2040 to 2100, the annual average sea level pressure for the various RCP scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). https://doi.org/10.1371/journal.pone.0334347.g014