Predicting coastal erosion susceptibility in Bangladesh under climate scenario via machine learning techniques

precipitation shows a strong negative correlation with both DtF (r = −0.95) and MSL (r = −0.87). There were also moderate correlations found between DEM and Geology (r = 0.52) and between Slope and DEM (r = 0.77). Fig 10 indicates that NDVI is the predominant factor in AdaBoost, CatBoost, GBDT, LightGBM, XGBoost, Bagging, DT, RF and TreeBag, with scores of 19, 22, 23, 16, 26, 22, 20, 28 and 24, respectively. Nonetheless, with a value of 20 the Aridity Index is the predominant factor in the avNNet model. The scores of 14, 14, 12, 16, 18 and 14 for the Aridity Index have a significant impact on AdaBoost, GBDT, LightGBM, Bagging, RF, and TreeBag. With scores of 14, 15 and 15 in CatBoost, XGBoost and Decision Trees, respectively, DtC also has a significant influence. Significantly, excluding avNNet, Geology and Soil Texture demonstrate the lowest significance across all models. Fig 8. Land use & Human activity factors (a) LULC, (b) NDVI. https://doi.org/10.1371/journal.pone.0334347.g008

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 18 / 34 Coastal erosion susceptibility zones distribution based on machine learning models Table 4 and Fig 11 illustrate the coastal erosion vulnerability zones categorized into low, moderate, and high impact levels. Table 4 demonstrates that there are comparable patterns among AdaBoost, GBDT, avNNet, and LightGBM, with a high percentage of moderate-impact areas (77.81%–79.36%), low-impact areas (around 10%–12.6%), and high-impact areas (9.58%–12.03%). CatBoost and XGBoost likewise exhibit similar outcomes, with high impact coming in slightly lower at 8.12%–8.81%, low impact areas at about 18%, and moderate impact predominating at 72.68% and 73.58%, respectively. Between 71.82% and 74.36%, the Bagging and DT models have moderate impact; Bagging has the largest low-impact area (19.03%), while DT has a noteworthy high-impact proportion (13.23%). With a moderate impact of almost 76%, RF and TreeBag exhibit comparable patterns; however, RF’s high-impact area is smaller (7.9%) than TreeBag’s (9.04%). Validation of models by ROC and AUC Fig 12 depicts the results of model validation through ROC curves, confusion matrices, F1-scores, precision, and recall, highlighting significant performance disparities. XGBoost proved to be the most effective model, attaining a maximum AUC of 0.95 and an F1-score of 0.81, with precision and recall metrics surpassing 0.68. Close competitors included Bagging and CatBoost, both attaining robust results with AUCs about 0.94 and F1-scores of 0.79. Models including

GBDT, Random Forest, and LightGBM demonstrated comparable efficacy, attaining AUCs of 0.93 and F1-scores of 0.78 and 0.80. Both ANN and Treebag demonstrated somewhat suboptimal performance, with AUCs of 0.92 and F1-scores between 0.73 and 0.78. AdaBoost demonstrated marginally inferior performance, achieving an AUC of 0.91 and an F1-score of 0.72. The Decision Tree model had the lowest evaluation, attaining an AUC of 0.78 and an F1-score slightly under 0.70. Table 3. Multicollinearity and Significance Testing for Model Variable Selection. Feature VIF Tolerance Pearson Correlation P-Value Aridity Index 3.48 0.29 0.11 0.001 Aspect 2.97 0.34 −0.03 0.016 Curvature 1.01 0.99 −0.01 0.603** DEM 6.15 0.16 0.09 0.001 DtC 1.34 0.75 −0.30 0.001 DtR 1.60 0.63 0.01 0.562** DtF 4.96 0.20 −0.15 0.001 Geology 7.84 0.13 0.10 0.001 Geomorphology 9.26 0.11 −0.16 0.001 LULC 9.06 0.11 0.02 0.389** Mean Wind Speed 3.13 0.32 0.12 0.001 MSL 459.99*** 0.00 −0.13 0.001 NDVI 9.31 0.11 −0.57 0.001 Precipitation 240.81*** 0.00 0.22 0.001 Slope 3.74 0.27 0.18 0.001 Soil Texture 7.96 0.13 0.05 0.021 TPI 1.53 0.65 −0.02 0.371** TRI 2.12 0.47 −0.07 0.001 TWI 1.33 0.75 −0.01 0.779** VDCN 2.89 0.35 −0.21 0.001 Note: The model excludes variables with p-values above 0.05 (**) and VIFs above 10 (***) because they are statistically insignificant. https://doi.org/10.1371/journal.pone.0334347.t003

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

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 20 / 34 Coastal erosion vulnerability zones distribution based on different future climate change scenarios Table 6 and Fig 15 present coastal erosion Susceptibility zones distributions under various climatic scenarios (RCP 2.6, 4.5, 6.0, and 8.5) for the years 2040, 2060, 2080, and 2100. In 2040, RCP 2.6 is characterized by a significant Low cate- gory (42%) and a minimal High category (3%), but RCP 4.5, 6.0, and 8.5 exhibit diverse distributions, with RCP 6.0 pre- senting the most substantial High category (35%). By 2060, RCP 2.6 and 8.5 indicate a substantial transition, with around 47% of the area classified as High, whilst RCP 4.5 and 6.0 also demonstrate an increasing trend in the High category; however, RCP 6.0 exhibits a more equitable distribution (13% Low, 53% Moderate, 34% High). By 2080, a comparable trend persists, with RCP 6.0 and 8.5 exhibiting the highest High category percentages at 48% and 50%, respectively, and RCP 2.6 and 4.5 are at 44% and 45%. By 2100, RCP 2.6, 4.5, and 6.0 exhibit a marked decrease in the High category (between 8% and 19%) and an increase in the Low category, especially in RCP 6.0 (31%). Simultaneously, RCP 8.5 with 40% of the area is still designated as High. Fig 10. Assessment of the importance of parameters that influence coastal erosion. https://doi.org/10.1371/journal.pone.0334347.g010

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 21 / 34 Discussion This study analytically evaluated coastal erosion susceptibility in Bangladesh using sophisticated machine learning algo- rithms that combined topographical, hydrological, climatic, geological, and human elements. The Normalized Difference Vegetation Index (NDVI) was identified as the most significant variable by feature selection procedures, specifically Pear- son correlation, variance inflation factor (VIF), and p-values. The significance of vegetation cover in reducing the danger of erosion was highlighted by the NDVI with up to 28% contribution in the Random Forest (RF) model and 26% in XGBoost. Another important component was the Aridity Index, which made up 20% of avNNet and 12–18% of ensemble configura- tions. With contributions of 15% in XGBoost and 14–16% in other models, Distance to Coast (DtC) continuously placed highly across all models. On the other hand, geological factors including soil texture and geology had lower significance ratings, usually less than 6%, suggesting that they had little bearing on the variability of erosion in space. These results are reliable with earlier research by R. Bera and R. Maiti [137] and Islam et al. [138], who also highlighted the supremacy of NDVI and coastal proximity in determining coastal susceptibility. The results, however, are in contrast to those of Gao et al. [139], whose modeling approach based on RUSLE gave geomorphological variables more weight. The deterministic

nature of RUSLE models, which lack the adaptive learning capability found in the machine learning approaches employed here, is probably the cause of the discrepancy. Ensemble machine learning methods consistently beat individual classifiers in terms of model performance. With an area under the curve (AUC) score of 0.95, XGBoost outperformed CatBoost and Bagging, which were closely followed by GBDT, RF, and LightGBM (0.93). With an AUC of only 0.78, simpler models such as Decision Tree performed poorly. High-risk zones ranged from 7.9% (RF) to 13.23% (DT), whereas moderate-risk zones, which ranged from 71.82% (Bag- ging) to 79.36% (GBDT), spanned the largest areas across all models. Particularly, the AUC values in this study surpass those reported by Islam et al. [138] for distinctive between high and low erosion susceptibility zones in the Chattogram coastal region, excellent model performance is indicated by the AUC value of 0.909. Additionally, Bammou et al. [140] documented an AUC of 0.96 for the XGBoost model in their soil erosion evaluation, corroborating the dependability of superior performance results in analogous situations. This suggests that the existing ensemble configurations for coastal erosion scenarios have a greater level of predictive dependability. A different result was reported by Rahmati et al. [141], for susceptibility mapping in Iran, Random Forest (AUC 0.91) performed somewhat better than XGBoost (AUC 0.89). This demonstrates how dataset properties and regional conditions can affect model performance.

According to the high-resolution susceptibility maps created for this study, Bhola (19.41%), Cox’s Bazar (26.20%), and Patuakhali (21.47%) are some of the districts that are most susceptible to erosion at the moment. Conversely, districts with large percentages of low-risk zones include Jashore (86.94%), Narail (71.24%), and Gopalganj (52.71%). This is Table 4. Coastal Erosion Susceptibility Zones Distribution Based on ML Models. Low Moderate High Percentage Area Percentage Area Percentage Area AdaBoost 9.28% 4380.12 78.69% 37143.66 12.03% 5676.21 CatBoost 18.50% 8733.79 72.68% 34306.06 8.81% 4160.16 GBDT 10.65% 5025.94 79.36% 37457.76 9.99% 4716.31 LightGBM 12.60% 5948.73 77.81% 36727.76 9.58% 4523.51 XGBoost 18.30% 8636.01 73.58% 34729.97 8.12% 3834.02 avNNet 10.02% 4730.51 79.20% 37381.93 10.78% 5087.56 Bagging 19.03% 8981.01 71.82% 33900.16 9.15% 4318.82 DT 12.41% 5855.37 74.36% 35100.18 13.23% 6244.46 RF 15.96% 7533.13 76.14% 35939.47 7.90% 3727.40 Treebag 14.37% 6784.35 76.59% 36148.45 9.04% 4267.21 https://doi.org/10.1371/journal.pone.0334347.t004

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 22 / 34 Fig 11. Maps of coastal erosion generated through machine learning methods (a) AdaBoost, (b) CatBoost, (c) GBDT, (d) LightGBM, (e) XGBoost, (f) ANN, (g) Bagging, (h)DT, (i)RF, (j) TreeBag. https://doi.org/10.1371/journal.pone.0334347.g011

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 23 / 34 Fig 12. Model validation with Receiver Operating Characteristic (ROC) curve and Confusion matrix for (a) AdaBoost, (b) CatBoost, (c) GBDT, (d) LightGBM, (e) XGBoost, (f) ANN, (g) Bagging, (h)DT, (i)RF, (j) Treebag. https://doi.org/10.1371/journal.pone.0334347.g012

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 24 / 34 probably because of things like higher elevation, more vegetation, and a more inland location. Many districts, such as Barguna (91.45%), and Feni (90.13%), are dominated by moderate-risk zones. The spatial distribution of erosion suscep- tibility resembles well with [142,143] who also recognized Patuakhali and Cox’s Bazar as areas for vulnerability. Although their research focused on tidal-driven riverbank erosion, the current findings point to a combined effect of low elevation, vegetation loss, and weather stresses. Anthropogenic and climatic factors, which are frequently missing from determinis- tic models like GloSEM and RUSLE, allow for this wider viewpoint. This makes the model a more complete tool for both current and future coastal erosion risk assessment. This research is distinct from previous studies that it combines CMIP5-based climate scenarios. The risk of coastal ero- sion is predicted to significantly increase under future climate scenarios, especially under high-emission pathways. High- risk areas are expected to rise from 26% in 2040 to a peak of 50% by 2080, then slightly fall to 40% by 2100, according to RCP 8.5. RCP 6.0 shows similar patterns, with high-risk zones reaching a peak of 48% by 2080. Peak high-risk area coverage, on the other hand, rises more moderately at RCP 2.6 and 4.5, reaching 47% and 45%, respectively. These forecasts highlight how severe coastal damage could occur in the event of unchecked climate change. These patterns

Fig 13. District-wise coastal erosion Susceptibility zones by the best model (XGBoost). https://doi.org/10.1371/journal.pone.0334347.g013

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 25 / 34 align with the work of [144], who documented faster coastline retreat in high sea-level rise scenarios. However, compared to the regional-scale estimates seen in previous work, the spatially explicit outputs of this analysis provide a more detailed view of risk distribution, increasing their usefulness for local adaptation planning. By combining machine learning, climate modeling, and geospatial analysis, this study creates a solid, data-driven framework for evaluating the danger of coastal erosion. The models are useful tools for predicting erosion under present and future conditions because of their excellent predictive ability and good spatial resolution. This hybrid methodology provides more generalizability and actionable insights for resilient coastal management and governance as compared to traditional approaches used in coastal Bangladesh. Although the modeling framework employed in this study is techni- cally transferable to other geographic locations, its predictability and interpretability will necessarily be region-dependent. Geomorphological variation including coral reefs, deltas, cliffs, and climatic regimes like monsoonal versus arid regimes, and land use necessitate extensive recalibration of the model using local input variables to yield significant and credi- ble results. Not only this but also the lack of real-time erosion monitoring could compromise temporal accuracy. Future

research should focus on the transfer and calibration of this framework across different geographic and climatic settings, employing local parameters, and also real-time monitoring to enhance model robustness, generalizability, and applicability across different coastal systems. Conclusion The security of Bangladesh’s shoreline is in danger because sea levels are rising faster, climate change is getting worse, and the land is becoming less stable. Using modern machine learning methods and 20 important meteorological, geo- graphical, hydrological, tropological, and land-use factors, this study looked at how Susceptibility the coast is to erosion. The main things that affect erosion risk are the normalized difference vegetation index (NDVI). To find areas that are likely to erode, different ensemble learning methods were used, such as bagging (Random Forest, Bagging, Decision Tree, Table 5. Coastal erosion Susceptibility zones distribution among districts in the Coastal region by the best model (XGBoost). Low Moderate High Percentage Area Percentage Area Percentage Area Bagerhat 10.30% 407.78 87.14% 3449.70 2.56% 101.52 Barguna 0.48% 8.72 91.45% 1674.38 8.08% 147.90 Barisal 21.83% 607.85 74.98% 2088.11 3.20% 89.04 Bhola 2.02% 68.70 78.58% 2673.93 19.41% 660.37 Chandpur 13.14% 232.67 85.94% 1397.33 0.91% 15.00 Chittagong 0.83% 43.72 88.40% 4669.95 10.78% 569.33 Cox’s Bazar 0.24% 5.88 73.57% 1833.26 26.20% 652.86 Feni 0.10% 0.98 90.13% 892.26 9.77% 96.76 Gopalganj 52.71% 774.35 47.17% 692.89 0.12% 1.76

Jashore 86.94% 2266.41 12.96% 337.94 0.10% 2.64 Jhalokati 20.27% 143.29 77.91% 550.85 1.82% 12.85 Khulna 18.92% 831.52 78.68% 3457.38 2.39% 105.11 Lakshmipur 4.52% 65.06 85.83% 1235.99 9.65% 138.94 Narail 71.24% 689.62 28.67% 277.49 0.09% 0.88 Noakhali 0.46% 16.98 82.60% 3044.53 16.94% 624.49 Patuakhali 0.60% 19.24 77.93% 2510.19 21.47% 691.57 Pirojpur 16.54% 211.44 80.86% 1033.41 2.59% 33.15 Satkhira 21.43% 817.90 74.74% 2852.90 3.83% 146.20 Shariatpur 46.78% 549.22 52.60% 617.56 0.61% 7.22 https://doi.org/10.1371/journal.pone.0334347.t005

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 27 / 34 coastal erosion management, since a lot of the high-risk zones coincide with marginalized groups. Policymakers must uti- lize these projections to create inclusive plans that reduce displacement risks and promote equitable access to resources. Incorporating community-based adaptation and livelihood assistance will be crucial for equitable and sustainable coastal resilience planning. Even though this study is very thorough, it has some limitations. If scholars do not take into account changing socio-economic factors like changes in land use and the building of new infrastructure, the risk of erosion might be underrated. Mitigation strategies such as population relocation can also disproportionately impact underprivileged peo- ple, resulting in loss of livelihoods, displacement, or limited access to critical services. Therefore, evaluations of erosion risk and policy measures must take into account socioeconomic and environmental factors in order to achieve inclusive and equitable results. To enhance predictive accuracy, future studies ought to make use of integrated socio-environmental databases, real-time monitoring, and high-resolution remote sensing. The UN’s Sustainable Development Goals (SDG) Table 6. Coastal erosion Susceptibility zones distribution according to Different RCP Scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) from 2040 to 2100 with Best Model. Low Moderate High 2040 RCP 2.6 % 42% 55% 3% Area 19906.75 25759.69 1533.564 RCP 4.5 % 16% 60% 24%

Area 7431.633 28403.71 11364.66 RCP 6.0 % 13% 52% 35% Area 6105.21 24414.56 16680.23 RCP 8.5 % 15% 59% 26% Area 7039.616 27928.71 12231.68 2060 RCP 2.6 % 9% 44% 47% Area 4268.691 20670.52 22260.79 RCP 4.5 % 11% 44% 45% Area 5121.893 20803.06 21275.04 RCP 6.0 % 13% 53% 34% Area 5980.069 25211.75 16008.18 RCP 8.5 % 10% 43% 47% Area 4653.039 20262.8 22284.16 2080 RCP 2.6 % 10% 45% 44% Area 4836.109 21417.6 20946.29 RCP 4.5 % 11% 45% 45% Area 5121.866 21024.81 21053.32 RCP 6.0 % 9% 43% 48% Area 4418.093 20203.93 22577.97 RCP 8.5 % 9% 41% 50% Area 4146.771 19279.35 23773.88 2100 RCP 2.6 % 19% 63% 19% Area 8842.319 29529 8828.68 RCP 4.5 % 28% 61% 11% Area 13215.88 28961.58 5022.535 RCP 6.0 % 31% 61% 8% Area 14515.5 28688.74 3995.765 RCP 8.5 % 13% 47% 40% Area 6250.205 22178.32 18771.48 https://doi.org/10.1371/journal.pone.0334347.t006

PLOS One | https://doi.org/10.1371/journal.pone.0334347 November 5, 2025 28 / 34 11 (Sustainable Cities and Communities) and 13 (Climate Action) are in line with this work. It gives useful information that could help with managing the coast and making plans for long-term protection in Bangladeshi places that are prone to erosion. Author contributions Conceptualization: Mafrid Haydar. Data curation: Sakib Hosan, Sondipon Dey Pranta, Tahdia Tahmid, Mafrid Haydar, Al Hossain Rafi. Formal analysis: Sakib Hosan, Sondipon Dey Pranta. Methodology: Sakib Hosan, Sondipon Dey Pranta, Mafrid Haydar, Al Hossain Rafi. Supervision: Mafrid Haydar. Validation: Sakib Hosan, Sondipon Dey Pranta. Fig 15. Coastal Erosion Susceptibility for Different RCP Scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) from 2040 to 2100 with Best Model (XGBoost). https://doi.org/10.1371/journal.pone.0334347.g015

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