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 12 / 34 to the set standards, values of area under the curve (AUC) range between 0 and 1, and high values indicate improved model discrimination capacities by a greater margin. A confusion matrix evaluates the effectiveness of a classification model by displaying true positives (nodes accurately identified as positive), true negatives (nodes accurately identified as negative), false positives (nodes inaccurately identi- fied as positive), and false negatives (nodes inaccurately identified as negative), thereby providing a thorough assessment of the model’s performance across different classes [128,129]. This matrix enables the assessment of model correctness, the detection of misclassifications, and the calculation of critical metrics such as the F1-score, which improves model performance. Accuracy represents the proportion of correct forecasts to total predictions, with increased accuracy indicat- ing greater overall correctness; Precision measures the ratio of true positives to total predicted positives, with high preci- sion signifying fewer false positives; recall evaluates the ratio of true positives to total actual positives, where high recall reduces false negatives; the F1-score integrates precision and recall as their harmonic mean, making it beneficial for assessing models, especially in imbalanced datasets [129]. Future climate change models Forecasts of impending climate change are required for the description of mechanisms of coastal erosion, for making esti-

mates of potential danger to coastal residents, and guiding the development of effective adaptation measures and mitiga- tions. The fifth Coupled Model Intercomparison Project (CMIP5) phase will provide a cutting-edge multimodel dataset that will advance our knowledge of climate variability and climate change [130]. The framework provides an excellent platform for climate variability assessment and its effects on water resources [131,132]. Representative Concentration Paths (RCPs) establish several greenhouse gas concentration pathways, such as a set of four new pathways created for the use of the climate modelling community as a basis for long-term and short-term modelling studies [133]. These RCPs serve as the basis for CMIP5 based projections wherein RCP 2.5, RCP 4.5, RCP 6.0, and RCP 8.5 represent the four RCP scenar- ios applied in this study. The purpose of this study is to evaluate future climate conditions and their implications that may occur [134]. Particularly, the research assesses the sea level average pressure under various scenarios using projection data for 2040, 2060, 2080, and 2100. Sea level rise causes the acceleration of the coastal erosion process by intensifying the frequency of wave impact on the coastlines, especially during storms and periods of high tides [135]. This results in the shoreline erosion process happening frequently and more intensely [136]. In this study, climate projection data were retrieved from the website of the NCAR GIS Initiative Climate Change Scenario (https://gisclimatechange.ucar.edu),

where projections of climate are provided for public access. Future coastal erosion projection A global climate model simulated projected coastal erosion risk zone maps for 2040, 2060, 2080, and 2100 using multiple climate change scenarios defined based on Representative Concentration Pathways (RCPs). A raster calculator was used to compute a spatial correlation between simulated coastal erosion effects and projected coastal erosion using outputs of a boosting model, i.e., XGBoost. The risk maps produced were then systematically put into various categories from low to highly vulnerable to coastal erosion. These projections provide useful insight into the expected increase in coastal vulnerability in Bangladesh and are a critical input to policymakers for formulating long-term adaptive shore protection and general disaster risk reduction strategies. Results and discussion Description of topographical factors The topographical factors in Fig 4 exhibit clear spatial changes throughout the region. Aspect(a) and Curvature(b) exhibit a mixed pattern with no distinct predominance in any specific area. DEM(c), Slope(e), and TPI(f) demonstrate elevated