Volume 12 Issue 3
Jul.  2021
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Abu Reza Md Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, Kutub Uddin Eibek, Quoc Bao Pham, Alban Kuriqi, Nguyen Thi Thuy Linh. Flood susceptibility modelling using advanced ensemble machine learning models[J]. Geoscience Frontiers, 2021, 12(3): 101075. doi: 10.1016/j.gsf.2020.09.006
Citation: Abu Reza Md Towfiqul Islam, Swapan Talukdar, Susanta Mahato, Sonali Kundu, Kutub Uddin Eibek, Quoc Bao Pham, Alban Kuriqi, Nguyen Thi Thuy Linh. Flood susceptibility modelling using advanced ensemble machine learning models[J]. Geoscience Frontiers, 2021, 12(3): 101075. doi: 10.1016/j.gsf.2020.09.006

Flood susceptibility modelling using advanced ensemble machine learning models

doi: 10.1016/j.gsf.2020.09.006

The authors would like to thank the USGS and other agencies of Bangladesh for providing the data for conducting this research. The authors also would like to acknowledge the Department of Geography, University of Gour Banga, India, for providing necessary facilities to carry out this research. The authors are grateful to the anonymous reviewers for their valuable comments and suggestions to improve this manuscript further. Alban Kuriqi was supported by a PhD scholarship granted by Fundação para a Ciência e a Tecnologia, I.P. (FCT), Portugal, under the PhD Programme FLUVIO-River Restoration and Management, grant number:PD/BD/114558/2016.

  • Received Date: 2020-05-13
  • Rev Recd Date: 2020-07-29
  • Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.
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