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 28 / 34 11 (Sustainable Cities and Communities) and 13 (Climate Action) are in line with this work. It gives useful information that could help with managing the coast and making plans for long-term protection in Bangladeshi places that are prone to erosion. Author contributions Conceptualization: Mafrid Haydar. Data curation: Sakib Hosan, Sondipon Dey Pranta, Tahdia Tahmid, Mafrid Haydar, Al Hossain Rafi. Formal analysis: Sakib Hosan, Sondipon Dey Pranta. Methodology: Sakib Hosan, Sondipon Dey Pranta, Mafrid Haydar, Al Hossain Rafi. Supervision: Mafrid Haydar. Validation: Sakib Hosan, Sondipon Dey Pranta. Fig 15. Coastal Erosion Susceptibility for Different RCP Scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) from 2040 to 2100 with Best Model (XGBoost). https://doi.org/10.1371/journal.pone.0334347.g015