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 3 / 34 an AUC of 0.932, RF was the top-performing model, followed by GBDT (0.893) and AdaBoost (0.902). Eloudi et al. [20] likened six models in mapping the susceptibility of gully erosion: AdaBoost, XGBoost, GBM, TreeBag, RF, and C5.0. With an AUC of 90.80%, the C5.0 model performed optimally, followed closely by RF (90.10%), XGBoost, and AdaBoost (both 90%). A number of studies have used traditional geospatial and statistical techniques to investigate erosion patterns and sug- gest conservation measures in Bangladesh. The Revised Universal Soil Loss Equation (RUSLE) with GIS has been used in a number of studies to predict soil erosion and detect erosion hotspots. Using the Global Soil Erosion Modeling Plat- form (GloSEM) Rahman et al. and Islam et al. [30,31] measured erosion rates and validated their findings. Despite yield- ing valuable information, these studies employed deterministic models assuming rigid links between erosion rates and input variables. This works against adaptive erosion management and reduces their potential to forecast future erosion scenarios in the face of changing climatic conditions. Socio-economic erosion research pays greater attention to adapta- tion and resilience compared to predictive modeling. Hasan et al. [32] used K-Nearest Neighbors (KNN), XGBoost, and Random Forest (RF) were utilized to evaluate flood risk in the coastal areas of Bangladesh. RF was the most accurate

with an accuracy of 86.7%, followed closely by XGBoost (86.3%). Barguna, Bhola, and Patuakhali districts were found to be particularly susceptible by the research. While Mamun et al. and Islam et. al [31,33] took into account coping mech- anisms and communal susceptibilities, they didn’t involve high-resolution spatial modeling for future hazard forecasting. However, there are a number of issues with existing approaches because deterministic models like RUSLE and GloSEM are based on static assumptions; they are unable to capture dynamic drivers like changing land use, extreme weather, and shifting climatic trends. Moreover, these models do not incorporate real-time data, thus limiting their use in adaptive erosion management. For better predictive accuracy, future research should emphasize the creation of hybrid models that combine machine learning and geographic methods. Furthermore, an integrated approach that combines socioeconomic risk analysis with physical erosion modeling in support of sustainable land management would provide holistic insight. Thus, the main goal of this research is to utilize an exhaustive set of machine learning (ML) models to evaluate the geographical aspects of coastal erosion in Bangladesh’s dynamic coastline by combining geological, climatic, hydrolog- ical, topographical, and human-induced parameters. This study aims to: (i) identify and rank the most important drivers controlling the susceptibility of coastal erosion; (ii) compare the predictive capabilities of different individual and ensemble-

based machine learning models for forecasting erosion-susceptible zones; (iii) generate high-resolution spatial suscep- tibility maps for the current scenario. The results aim to inform planners, environmental managers, and policymakers to implement well-informed, spatially explicit plans for the sustainable management of coastal zones in the face of present and future climate change. Materials and methods Overview of the study location The research area (in Fig 1) is situated in the coastal region of Bangladesh, in the southern part of the country, lying between longitudes 80° 0’ E to 91° 0’ E and latitudes 21° 30’ N to 22° 30’ N [34]. The coastal region of Bangladesh covers 47,200 km², including land, islands, and several water bodies, constituting around 32% of the nation’s total area [35]. It is subdivided between the interior coast (23,265 km²) and the exposed coast (23,935 km²), with the latter being directly influenced by tidal surges and cyclonic activity from the Bay of Bengal [36]. The coastal zone of the nation is divided into three zones: western, central, and eastern, based on geomorphological characteristics, spanning approximately 27,150 km², 12,040 km², and 8,010 km² of coastal land area, respectively [37]. The eastern coast of Bangladesh is characterized by lofty, hilly land with urbanization and intricate flood dynamics; the middle portion is low-lying and extremely vulnerable to tidal and river floods; whereas the western region is covered with mangrove forests and dynamic delta landscape that

is prone to erosion and saltwater inundation [35,37,38]. The study aims at the coastal belt of Bangladesh, where erosion threatens human habitation and natural ecosystems significantly [30]. The region’s susceptibility to tidal surges, storm