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 33 / 34 103. Liaw A, Wiener M. The R Journal: Classification and regression by randomForest. R Journal. 2002;2(3):18–22. 104. Conesa A, Hernández R. Omics Data Integration in Systems Biology. Comprehensive Analytical Chemistry. Elsevier. 2014. p. 441–59. https://doi. org/10.1016/b978-0-444-62650-9.00016-6 105. Richman R, Wüthrich MV. Nagging Predictors. Risks. 2020;8(3):83. https://doi.org/10.3390/risks8030083 106. Ngo G, Beard R, Chandra R. Evolutionary bagging for ensemble learning. Neurocomputing. 2022;510:1–14. https://doi.org/10.1016/j. neucom.2022.08.055 107. Chan JC-W, Paelinckx D. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment. 2008;112(6):2999–3011. https://doi.org/10.1016/j. rse.2008.02.011 108. Korecki JN, Banfield RE, Hall LO, Bowyer KW, Kegelmeyer WP. Semi-supervised learning on large complex simulations. In: 2008 19th Interna- tional Conference on Pattern Recognition. 2008. 1–4. https://doi.org/10.1109/icpr.2008.4761797 109. Chollet F. Machine learning 분야 소개 및 주요 방법론 학습 기본 machine learning 알고리즘에 대한 이해 및 응용 관련 최신 연구 동향 습득. Mach Learn. 2017;45(13):40–8. 110. Cho JH, Kurup PU. Decision tree approach for classification and dimensionality reduction of electronic nose data. Sensors and Actuators B: Chemical. 2011;160(1):542–8. https://doi.org/10.1016/j.snb.2011.08.027

111. Packwood D, Nguyen LTH, Cesana P, Zhang G, Staykov A, Fukumoto Y, et al. Machine Learning in Materials Chemistry: An Invitation. Machine Learning with Applications. 2022;8:100265. https://doi.org/10.1016/j.mlwa.2022.100265 112. Murthy SK. Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery. 1998;2(4):345–89. https://doi.org/10.1023/a:1009744630224 113. Ahmed AM, Rizaner A, Ulusoy AH. A Decision Tree Algorithm Combined with Linear Regression for Data Classification. In: 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). 2018. 1–5. https://doi.org/10.1109/iccceee.2018.8515759 114. Scornet E. On the asymptotics of random forests. Journal of Multivariate Analysis. 2016;146:72–83. https://doi.org/10.1016/j.jmva.2015.06.009 115. Altman N, Krzywinski M. Ensemble methods: bagging and random forests. Nat Methods. 2017;14(10):933–4. https://doi.org/10.1038/nmeth.4438 116. Breiman L. Bagging Predictors. Machine Learning. 1996;24(2):123–40. https://doi.org/10.1023/a:1018054314350 117. Oza NC. Online bagging and boosting. Conf Proc IEEE Int Conf Syst Man Cybern. 2005;3(2001):2340–2345. https://doi.org/10.1109/ icsmc.2005.1571498 118. Kleinberg EM. On the algorithmic implementation of stochastic discrimination. IEEE Trans Pattern Anal Machine Intell. 2000;22(5):473–90. https:// doi.org/10.1109/34.857004 119. Quinlan JR. Combining Instance-Based and Model-Based Learning. Machine Learning Proceedings 1993. Elsevier. 1993. p. 236–43. https://doi.

org/10.1016/b978-1-55860-307-3.50037-x 120. Yang S, Browne A. Neural network ensembles: combining multiple models for enhanced performance using a multistage approach. Expert Sys- tems. 2004;21(5):279–88. https://doi.org/10.1111/j.1468-0394.2004.00285.x 121. Burton TA. Averaged neural networks. 1993;6(3):677–80. 122. Hansen LK, Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Machine Intell. 1990;12(10):993–1001. https://doi. org/10.1109/34.58871 123. Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning Anders. 2010. 6–7. 124. Zhou Z-H, Wu J, Tang W. Corrigendum to “Ensembling neural networks: Many could be better than all” Artificial Intelligence 137 (1–2) (2002) 239–263. Artificial Intelligence. 2010;174(18):1570. https://doi.org/10.1016/j.artint.2010.10.001 125. Tien Bui D, Pham BT, Nguyen QP, Hoang N-D. Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least- Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam. International Journal of Digital Earth. 2016;9(11):1077–97. https://doi.org/10.1080/17538947.2016.1169561 126. Richardson E, Trevizani R, Greenbaum JA, Carter H, Nielsen M, Peters B. The receiver operating characteristic curve accurately assesses imbal- anced datasets. Patterns (N Y). 2024;5(6):100994. https://doi.org/10.1016/j.patter.2024.100994 PMID: 39005487 127. Haydar M, Hosan S, Rafi AH. Assessment of urban expansion susceptibility in major urban units of Bangladesh leveraging machine learning and

geostatistical approach. Journal of Urban Management. 2025;14(2):451–67. https://doi.org/10.1016/j.jum.2024.11.011 128. Mazumder T, Saroar MdM. Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach. Progress in Disaster Science. 2025;25:100406. https://doi.org/10.1016/j.pdisas.2025.100406 129. Riehl K, Neunteufel M, Hemberg M. Hierarchical confusion matrix for classification performance evaluation. Journal of the Royal Statistical Soci- ety Series C: Applied Statistics. 2023;72(5):1394–412. https://doi.org/10.1093/jrsssc/qlad057 130. Taylor KE, Stouffer RJ, Meehl GA. An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society. 2012;93(4):485–98. https://doi.org/10.1175/bams-d-11-00094.1 131. Sarkar SK, Rudra RR, Talukdar S, Das PC, Nur MS, Alam E, et al. Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh. Sci Rep. 2024;14(1):10328. https://doi.org/10.1038/s41598-024-60560-2 PMID: 38710767