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 4 / 34 effects, and sea level rise at high levels renders it a significant place for assessing erosion risk [39]. This study aims to enhance the predictive accuracy and establish a sustainable coastal erosion mitigation framework in Bangladesh through the application of advanced machine learning algorithms and GIS-based susceptibility modeling. The research employs high-resolution geographical data and climate change scenarios to more accurately identify locations susceptible to ero- sion. This integrative approach safeguards Susceptibility ecosystems and societies on the coast of Bangladesh by cre- ating a scientific basis to formulate sustainable coastal management policies and long-term adaptation strategies, while facilitating evidence-based decision-making. Description of data The model parameters were chosen and identified following a comprehensive review of the literature. Twenty factors mentioned in Table 1 are employed for reducing coastal erosion in Bangladesh. Digital Elevation Models (DEMs) help pinpoint low-lying coastal regions susceptible to sea-level rise, tidal inundation, and wave erosion, where low-lying places are more Susceptibility to erosion because of their closeness to water and coastal dynamics, whereas elevated regions Fig 1. Study area map of the coastal region of Bangladesh. https://doi.org/10.1371/journal.pone.0334347.g001