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 19 / 34 Coastal erosion susceptibility zones distribution by best model among districts The geographical findings that are derived from the analysis of the XGBoost model presented in Fig 13 and Table 5 reveal that the Jashore and Narail districts possess the highest percentage of areas that are classified as low-risk. The districts take up 86.94% and 71.24% of the respective areas. Shariatpur (46.78%) and Gopalganj (52.71%) are primarily the contributors of this kind of low-risk investment. Districts like Barguna, Chandpur, Feni, and Lakshmipur are predominantly endowed with moderate-risk classes, with a high 91.45% of Barguna’s area falling into this category. Spatial patterns, however, indicate that districts like these are largely dominated by these classes. Bagerhat, Khulna, Pirojpur, and Jhalo- kati are typical districts with high coverage levels in moderate risks, with all of them exceeding 77%. The spatial pattern reveals that Bhola (19.41%), Cox’s Bazar (26.20%), and Patuakhali (21.47%) have the highest percentage of high-risk areas, thus being the most vulnerable districts. Moreover, districts like Noakhali (16.94%) and Satkhira (3.83%) have sig- nificant areas of high-risk areas, which signify their vulnerability in the local risk scenario. Future climate change scenarios According to Fig 14, sea level pressure maps across several RCP scenarios demonstrate that the eastern region exhibits

more sea level pressure compared to the western region. In 2040, RCP 2.6 demonstrates a slightly elevated sea level pressure relative to RCP 4.5, 6.0, and 8.5. In 2060, a universal decrease in sea level pressure is noted across all scenar- ios. By 2080, RCP 8.5 forecasts slightly reduced pressure compared to the other scenarios. By 2100, all Representative Concentration Pathways (RCPs) indicate a little upward trend in sea level pressure. Fig 9. Correlation Matrix of all variables. https://doi.org/10.1371/journal.pone.0334347.g009