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Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries

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Abstract

Flood, a dangerous hydro-geomorphic hazard, is one of the most critically applied science research issue. The restoration and recovery are costly and can interrupt communities’ sustainable growth after the extensive flood. Flash floods (FF) are a frequent natural disaster that causes significant casualties and disrupts economic growth in the Brahmaputra River Basin (BRB). Hence, the flood susceptibility modeling of BRB is imperative. The study uses six machine learning (ML) techniques (three stand-alone such as artificial neural network (ANN), fuzzy logic (FL), and random forest (RF), and three hybrid ensemble models (HEMs) including ANN-FL, FL-RF, and RF-ANN) to appraise flash flood Susceptibility (FFS) prediction in BRB considering 16 flash flood susceptibility factors. Area under the curve (AUC), ROC curve, confusion matrix (CM), and Friedman test are applied to assess the performance of the models. Results for the training and testing datasets showed that all HEMs models for FFS prediction in the BRB outperformed the stand-alone models. The RF-ANN has the best prediction ability of all models because the RF meta-classifier improves the ANN model’s base-classifier precision. The RF-ANN model delineated 2908.46 km2 and 874.73 km2 areas as very high and high flood susceptible zones, whereas 995.99 km2, 702.48 km2, and 10,127.57 km2 areas were predicted as moderate, low, and very low flood susceptible zones. Slope, water, vegetation, PrC, aspect, and rainfall all make the BRB sensitive to FF, as per the analysis of InGR and PCM. This work’s accuracy of the ML HEMs used for FFS mapping is promising. Furthermore, the findings of this study may be valuable for flood prevention and management to deal with the current uncertainties and more precisely identify numerous characteristics that impact FFS. This research is helpful for policymakers because it provides information that could be utilized to develop measures to lessen the adverse effects of FF.

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Data and materials availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Group under grant number RGP2/363/44. The authors are also thankful to the USGS Earth Explorer for making the LANDSAT data freely available.

Funding

Funding for this research was given under award numbers RGP2/363/44 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.

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Authors and Affiliations

  1. Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh

    Abu Reza Md. Towfiqul Islam & Md. Mijanur Rahman Bappi

  2. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia

    Saeed Alqadhi & Javed Mallick

  3. Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, Kingdom of Saudi Arabia

    Ahmed Ali Bindajam

  4. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, 110025, India

    Swapan Talukdar

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  1. Abu Reza Md. Towfiqul Islam
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  2. Md. Mijanur Rahman Bappi
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  3. Saeed Alqadhi
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  4. Ahmed Ali Bindajam
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  5. Javed Mallick
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  6. Swapan Talukdar
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Contributions

Abu Reza Md. Towfiqul Islam, Swapan Talukdar and Md. Mijanur Rahman Bappi contributed to conceptualization, data curation, methodology, and writing—original draft; Md. Mijanur Rahman Bappi and Saeed Alqadhi were involved in formal analysis; Javed Mallick, Saeed Alqadhi, Ahmed Ali Bindajam contributed to funding acquisition; Saeed Alqadhi and Ahmed Ali Bindajam were involved in project administration and resources; Javed Mallick and Swapan Talukdar provided software and contributed to writing—review and editing; Abu Reza Md. Towfiqul Islam and Javed Mallick was involved in supervision; and Abu Reza Md. Towfiqul Islam contributed to validation.

Corresponding author

Correspondence to Javed Mallick.

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Islam, A.R.M.T., Bappi, M.M.R., Alqadhi, S. et al. Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries. Nat Hazards 119, 1–37 (2023). https://doi.org/10.1007/s11069-023-06106-7

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