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Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms

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Abstract

The flooding in Bangladesh during monsoon season is very common and frequently happens. Consequently, people have been experiencing tremendous damage to properties, infrastructures, and human casualties. Usually, floods are one of the devastating disasters from nature, but for developing nations like Bangladesh, flooding becomes worse. Due to the dynamic and complex nature of the flooding, the prediction of flooding sites was usually very difficult for flood management. But the artificial intelligence and advanced remote sensing techniques together could predict and identify the possible sites, which are vulnerable to flooding. The present work aimed to predict and identify the flooding sites or flood susceptible zones in the Teesta River basin by employing state-of-the-art novel ensemble machine learning algorithms. We developed ensembles of bagging with REPtree, random forest (RF), M5P, and random tree (RT) algorithms for obtaining reliable and highly accurate results. Twelve factors, which are considered as the conditioning factors, and 413 current and former flooding points were identified for flooding susceptibility modelling. The Information Gain ratio statistical technique was utilized to determine the influence of the factors for flooding. We applied receiver operating characteristic curve (ROC) for validation of the flood susceptible models. The Freidman test, Wilcoxon signed-rank test, Kruskal–Wallis test and Kolmogorov–Smirnov test were applied together for the first time in flood susceptibility modelling to compare the models with each other. Results showed that more than 800 km2 area was predicted as the very high flood susceptibility zones by all algorithms. The ROC curve showed that all models achieved more than 0.85 area under the curve indicating highly accurate flood models. For flood susceptibility modelling, the bagging with M5P performed superior, followed by bagging with RF, bagging with REPtree and bagging with RT. The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate measures to avoid potential damages.

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Authors and Affiliations

  1. Department of Geography, University of Gour Banga, Malda, West Bengal, India

    Swapan Talukdar & Susanta Mahato

  2. Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

    Bonosri Ghose & Roquia Salam

  3. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, 110025, New Delhi, India

    Shahfahad

  4. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Quoc Bao Pham

  5. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Quoc Bao Pham

  6. Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam

    Nguyen Thi Thuy Linh

  7. Faculty of Environmental and Chemical Engineering, Duy Tan University, Danang 550000, Vietnam

    Nguyen Thi Thuy Linh

  8. Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, Bucharest, Romania

    Romulus Costache

  9. National Institute of Hydrology and Water Management, Bucureti-Ploieti Road, 97E, 1st District, 013686, Bucharest, Romania

    Romulus Costache

  10. Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, P.O. Box 14115-111, Tehran, Iran

    Mohammadtaghi Avand

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  1. Swapan Talukdar
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  2. Bonosri Ghose
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  3. Shahfahad
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  4. Roquia Salam
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  5. Susanta Mahato
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  6. Quoc Bao Pham
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  7. Nguyen Thi Thuy Linh
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  8. Romulus Costache
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  9. Mohammadtaghi Avand
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Correspondence to Quoc Bao Pham.

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Talukdar, S., Ghose, B., Shahfahad et al. Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stoch Environ Res Risk Assess 34, 2277–2300 (2020). https://doi.org/10.1007/s00477-020-01862-5

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