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 27 / 34 coastal erosion management, since a lot of the high-risk zones coincide with marginalized groups. Policymakers must uti- lize these projections to create inclusive plans that reduce displacement risks and promote equitable access to resources. Incorporating community-based adaptation and livelihood assistance will be crucial for equitable and sustainable coastal resilience planning. Even though this study is very thorough, it has some limitations. If scholars do not take into account changing socio-economic factors like changes in land use and the building of new infrastructure, the risk of erosion might be underrated. Mitigation strategies such as population relocation can also disproportionately impact underprivileged peo- ple, resulting in loss of livelihoods, displacement, or limited access to critical services. Therefore, evaluations of erosion risk and policy measures must take into account socioeconomic and environmental factors in order to achieve inclusive and equitable results. To enhance predictive accuracy, future studies ought to make use of integrated socio-environmental databases, real-time monitoring, and high-resolution remote sensing. The UN’s Sustainable Development Goals (SDG) Table 6. Coastal erosion Susceptibility zones distribution according to Different RCP Scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) from 2040 to 2100 with Best Model. Low Moderate High 2040 RCP 2.6 % 42% 55% 3% Area 19906.75 25759.69 1533.564 RCP 4.5 % 16% 60% 24%

Area 7431.633 28403.71 11364.66 RCP 6.0 % 13% 52% 35% Area 6105.21 24414.56 16680.23 RCP 8.5 % 15% 59% 26% Area 7039.616 27928.71 12231.68 2060 RCP 2.6 % 9% 44% 47% Area 4268.691 20670.52 22260.79 RCP 4.5 % 11% 44% 45% Area 5121.893 20803.06 21275.04 RCP 6.0 % 13% 53% 34% Area 5980.069 25211.75 16008.18 RCP 8.5 % 10% 43% 47% Area 4653.039 20262.8 22284.16 2080 RCP 2.6 % 10% 45% 44% Area 4836.109 21417.6 20946.29 RCP 4.5 % 11% 45% 45% Area 5121.866 21024.81 21053.32 RCP 6.0 % 9% 43% 48% Area 4418.093 20203.93 22577.97 RCP 8.5 % 9% 41% 50% Area 4146.771 19279.35 23773.88 2100 RCP 2.6 % 19% 63% 19% Area 8842.319 29529 8828.68 RCP 4.5 % 28% 61% 11% Area 13215.88 28961.58 5022.535 RCP 6.0 % 31% 61% 8% Area 14515.5 28688.74 3995.765 RCP 8.5 % 13% 47% 40% Area 6250.205 22178.32 18771.48 https://doi.org/10.1371/journal.pone.0334347.t006