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 2 / 34 Introduction As waves continuously alter the morphology of the shore over time, coastal erosion gradually erodes away the land. The high rate of coastal erosion significantly raises coastal susceptibility at the national, regional, and international levels in addition to other coastal disasters such as tidal surge, cyclones, and flooding [1,2]. For people who live in coastal, deltaic, and riverine systems worldwide, erosion poses serious environmental problems [3–6]. It is typically the result of both natural and anthro- pogenic forces, which may work alone or in concert [7,8]. Anthropogenic factors, including building ports, coastal barriers, groins, waterways, tourist hotels, gardens, and dredging inlets, as well as natural ones like the wind, surges, currents, particles, active tectonics, and sea level rise, all contribute to coastal erosion [9]. The shape of the coasts is permanently altered when the sediment is removed from the sediment sharing system [10]. Despite the fact that no part of the planet is immune to environ- mental dangers, certain places are geographically more susceptible to their impacts than others. For instance, the low-lying coastal areas are extremely susceptible due to the effects of climate change [11]. Although the effects of coastal erosion are lim- ited to coastal regions, these regions are home to more than 40% of the global popu- lation and a diverse spectrum of coastal ecosystems that offer a multitude of services

[12]. The coastal areas of Bangladesh are no exception to this reality, as they cover 32% of the terrestrial area of Bangladesh and are home to 26% of the total popu- lation of the country [13]. Bangladesh loses 34 square kilometers of land per year, and this loss could get worse as sea levels rise [14]. Coastal regions, especially mangrove forests, are prime candidates for protection [15]. Along the coast, loss of land is common, displacing people, disrupting livelihoods, and imperiling valuable ecosystems like the Sundarbans, the world’s largest mangrove forest. Coastal resil- ience is one of the priority sustainable development issues of Bangladesh because of intensifying cyclones, increased salinity intrusion, and unpredictable weather patterns caused by climate change. Various techniques have been used by the researchers, which include expert- based methods and real-time data analysis. As alternative methods for the prediction of beach morpho dynamics, different machine learning methods have been devel- oped and tested over the last several years. In some studies, researcher have trained and validated models such as the Functional Trees (FT), Multi-Layer Perceptron (MLP), Naïve Bayes (NB), Support Vector Machines (SVM), Logistic Regression (LR) and Deep Learning (DL) with the physical and environmental parameters such as wave height and direction, horizontal flow magnitude, geology, and slope, along with coastal erosion inventories [16–18]. The Random Forest (RF), Gradient Boosted Regression Tree (GBRT), and Tree Ensemble (TE) machine learning models were

used by [19–23] to identify the zones of maximum gully erosion (GES). Some other studies [24–28] focused on three deep learning techniques: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long-Short Term Memory (LSTM) for spatial forecasting. In research by Naceur et al. [29], outlined the N’fis River Basin in Morocco’s gully erosion using RF, AdaBoost, and GBDT models. With Competing interests: The authors have declared that no competing interests exist.