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 20 / 34 Coastal erosion vulnerability zones distribution based on different future climate change scenarios Table 6 and Fig 15 present coastal erosion Susceptibility zones distributions under various climatic scenarios (RCP 2.6, 4.5, 6.0, and 8.5) for the years 2040, 2060, 2080, and 2100. In 2040, RCP 2.6 is characterized by a significant Low cate- gory (42%) and a minimal High category (3%), but RCP 4.5, 6.0, and 8.5 exhibit diverse distributions, with RCP 6.0 pre- senting the most substantial High category (35%). By 2060, RCP 2.6 and 8.5 indicate a substantial transition, with around 47% of the area classified as High, whilst RCP 4.5 and 6.0 also demonstrate an increasing trend in the High category; however, RCP 6.0 exhibits a more equitable distribution (13% Low, 53% Moderate, 34% High). By 2080, a comparable trend persists, with RCP 6.0 and 8.5 exhibiting the highest High category percentages at 48% and 50%, respectively, and RCP 2.6 and 4.5 are at 44% and 45%. By 2100, RCP 2.6, 4.5, and 6.0 exhibit a marked decrease in the High category (between 8% and 19%) and an increase in the Low category, especially in RCP 6.0 (31%). Simultaneously, RCP 8.5 with 40% of the area is still designated as High. Fig 10. Assessment of the importance of parameters that influence coastal erosion. https://doi.org/10.1371/journal.pone.0334347.g010