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 32 / 34 75. Girma R, Gebre E. Spatial modeling of erosion hotspots using GIS-RUSLE interface in Omo-Gibe river basin, Southern Ethiopia: implication for soil and water conservation planning. Environ Syst Res. 2020;9(1). https://doi.org/10.1186/s40068-020-00180-7 76. Hernando D, Romana MG. Development of a Soil Erosion Classification System for Cut and Fill Slopes. Transp Infrastruct Geotech. 2015;2(4):155–66. https://doi.org/10.1007/s40515-015-0024-9 77. Nosrati K, Collins AL. A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catch- ment, Iran. J Mt Sci. 2019;16(11):2577–90. https://doi.org/10.1007/s11629-019-5567-8 78. Jorgenson MT, Brown J. Classification of the Alaskan Beaufort Sea Coast and estimation of carbon and sediment inputs from coastal erosion. Geo-Mar Lett. 2004;25(2–3):69–80. https://doi.org/10.1007/s00367-004-0188-8 79. Qi L, Zhou Y, Van Oost K, Ma J, van Wesemael B, Shi P. High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach. Geoderma. 2024;446:116905. https://doi.org/10.1016/j.geoderma.2024.116905 80. Shrestha N. Detecting Multicollinearity in Regression Analysis. AJAMS. 2020;8(2):39–42. https://doi.org/10.12691/ajams-8-2-1 81. Wagavkar S. Correlation matrix. IEEE Transactions on Computers. https://builtin.com/data-science/correlation-matrix

82. O’brien RM. A Caution Regarding Rules of Thumb for Variance Inflation Factors. Qual Quant. 2007;41(5):673–90. https://doi.org/10.1007/ s11135-006-9018-6 83. Ma M, Zhao G, He B, Li Q, Dong H, Wang S, et al. XGBoost-based method for flash flood risk assessment. Journal of Hydrology. 2021;598:126382. https://doi.org/10.1016/j.jhydrol.2021.126382 84. Chen T, Guestrin C. XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. 785–94. https://doi.org/10.1145/2939672.2939785 85. Haydar M, Rafi AH, Hasan Khan MdK, Hosan S, Sadia H. Mapping climate change induced forest fire susceptibility using tree-based machine learning algorithms in Bangladesh. Ecological Indicators. 2025;176:113657. https://doi.org/10.1016/j.ecolind.2025.113657 86. Haydar M, Hossain Rafi A, Sadia H, Tanvir Hossain M. Data driven forest fire susceptibility mapping in Bangladesh. Ecological Indicators. 2024;166:112264. https://doi.org/10.1016/j.ecolind.2024.112264 87. Arif Ali Z, H. Abduljabbar Z, A. Tahir H, Bibo Sallow A, Almufti SM. eXtreme Gradient Boosting Algorithm with Machine Learning: a Review. Acad J Nawroz Univ. 2023;12(2):320–34. https://doi.org/10.25007/ajnu.v12n2a1612 88. Huang X, Li Z, Jin Y, Zhang W. Fair-AdaBoost: Extending AdaBoost method to achieve fair classification. Expert Systems with Applications. 2022;202:117240. https://doi.org/10.1016/j.eswa.2022.117240 89. Mosavi A, Hosseini FS, Choubin B. Ensemble boosting and bagging based machine learning models for groundwater potential prediction. 2020.

90. Hastie T, Rosset S, Zhu J, Zou H. Multi-class AdaBoost. Statistics and Its Interface. 2009;2(3):349–60. https://doi.org/10.4310/sii.2009.v2.n3.a8 91. Kégl B, Busa-Fekete R. Boosting products of base classifiers. In: Proceedings of the 26th Annual International Conference on Machine Learning, 2009. 497–504. https://doi.org/10.1145/1553374.1553439 92. Haidong C, Shangqi D, Xingke G, Shuangde H, Tao W, Debin X, et al. Multi-temporal remote sensing fire detection based on GBDT in Yunnan area. In: 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), 2020. 469–73. https://doi. org/10.1109/mlbdbi51377.2020.00099 93. Wen Z, He B, Kotagiri R, Lu S, Shi J. Efficient Gradient Boosted Decision Tree Training on GPUs. In: 2018 IEEE International Parallel and Distrib- uted Processing Symposium (IPDPS). 2018. 234–43. https://doi.org/10.1109/ipdps.2018.00033 94. Shu Y, Shi C, Yi B, Zhao P, Guan L, Zhou M. Influence of Climatic Factors on Lightning Fires in the Primeval Forest Region of the Northern Dax- ing’an Mountains, China. Sustainability. 2022;14(9):5462. https://doi.org/10.3390/su14095462 95. Friedman JH. Greedy function approximation: A gradient boosting machine. Ann Statist. 2001;29(5). https://doi.org/10.1214/aos/1013203451 96. Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, et al. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Sci Rep. 2022;12(1):15889. https://doi.org/10.1038/s41598-022-20149-z PMID: 36220875

97. Ileri K. Comparative analysis of CatBoost, LightGBM, XGBoost, RF, and DT methods optimised with PSO to estimate the number of k-barriers for intrusion detection in wireless sensor networks. Int J Mach Learn & Cyber. 2025;16(9):6937–56. https://doi.org/10.1007/s13042-025-02654-5 98. Omotehinwa TO, Oyewola DO, Moung EG. Optimizing the light gradient-boosting machine algorithm for an efficient early detection of coronary heart disease. Informatics and Health. 2024;1(2):70–81. https://doi.org/10.1016/j.infoh.2024.06.001 99. Machado MR, Karray S, de Sousa IT. LightGBM: an Effective Decision Tree Gradient Boosting Method to Predict Customer Loyalty in the Finance Industry. In: 2019 14th International Conference on Computer Science & Education (ICCSE). 2019. 1111–6. https://doi.org/10.1109/ iccse.2019.8845529 100. Gan M, Pan S, Chen Y, Cheng C, Pan H, Zhu X. Application of the Machine Learning LightGBM Model to the Prediction of the Water Levels of the Lower Columbia River. JMSE. 2021;9(5):496. https://doi.org/10.3390/jmse9050496 101. Hancock JT, Khoshgoftaar TM. CatBoost for big data: an interdisciplinary review. J Big Data. 2020;7(1):94. https://doi.org/10.1186/s40537-020- 00369-8 PMID: 33169094 102. Zhang Y, Zhao Z, Zheng J. CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. Journal of Hydrology. 2020;588:125087. https://doi.org/10.1016/j.jhydrol.2020.125087