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 1 / 34 OPEN ACCESS Citation: Hosan S, Pranta SD, Tahmid T, Haydar M, Rafi AH (2025) Predicting coastal erosion susceptibility in Bangladesh under climate scenario via machine learning techniques. PLoS One 20(11): e0334347. https://doi.org/10.1371/journal.pone.0334347 Editor: Suresh Devaraj, Sathyambama Institute of Science and Technology: Sathyabama Institute of Science and Technology (Deemed to be University), INDIA Received: July 13, 2025 Accepted: September 25, 2025 Published: November 5, 2025 Copyright: © 2025 Hosan et al . This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data availability statement: Within the manu- script itself. Funding: The author(s) received no specific funding for this work. RESEARCH ARTICLE Predicting coastal erosion susceptibility in Bangladesh under climate scenario via machine learning techniques Sakib Hosan, Sondipon Dey Pranta, Tahdia Tahmid, Mafrid Haydar *, Al Hossain Rafi Department of Urban and Regional Planning, Khulna University of Engineering & Technology (KUET), Khulna, Bangladesh * mafrid@urp.kuet.ac.bd Abstract Using advanced machine learning methods along with geospatial data and climate estimates, this study found areas in Bangladesh that are likely to experience coastal erosion. Twenty important factors were looked at, such as meteorological, geograph-

ical, hydrological, tropological, and land-use variables. The normalized difference vegetation index (NDVI) was found to be the most important factor. An ensemble machine learning technique was used to figure out how susceptible coastal areas are to erosion. Several types of boosting techniques were used, including Extreme Gra- dient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Gradient Boosted Decision Trees (GBDT), and AdaBoost. Random Forest, Decision Tree, Treebag, Bagging, and Averaged Neural Network (avNNet) were also used. The area under the curve (AUC) and receiver operating characteristic (ROC) values were used to check how well the model worked. XGBoost had the best AUC, at 0.95, which means it did a very good job of classifying places that are likely to be washed away by erosion. The study of geography showed that most of the models showed moderate-risk areas, which made up 71.82% to 79.36% of the whole area. Some of the districts that were identified as mostly high-risk were Bhola (19.41%), Cox’s Bazar (26.20%), and Patuakhali (21.47%). A lot of low-risk zones were found in places like Jashore and Narail, on the other hand. Predictions of how likely erosion will be in the future based on different warming models to make Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5) for the years 2040, 2060, 2080, and 2100, we used data from the Coupled Model Intercomparison Project (CMIP5). In line with RCP 8.5, the number of high-risk places is expected to

rise to 50% by 2080 and to 40% by 2100. RCP 6.0 had a smooth shift, and in high-risk areas, there were only small rises. RCP 2.6, 4.5, and 6.0 all went up a little. These results show that Bangladesh’s shore is becoming more likely to be worn away by erosion as temperatures rise. They stress how important it is to quickly react, handle coastal areas, and use planning methods that are resilient to climate change.