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

Author

Listed:
  • Abu Reza Md. Towfiqul Islam

    (Begum Rokeya University)

  • Md. Mijanur Rahman Bappi

    (Begum Rokeya University)

  • Saeed Alqadhi

    (King Khalid University)

  • Ahmed Ali Bindajam

    (King Khalid University)

  • Javed Mallick

    (King Khalid University)

  • Swapan Talukdar

    (Jamia Millia Islamia)

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.

Suggested Citation

  • Abu Reza Md. Towfiqul Islam & Md. Mijanur Rahman Bappi & Saeed Alqadhi & Ahmed Ali Bindajam & Javed Mallick & Swapan Talukdar, 2023. "Improvement of flood susceptibility mapping by introducing hybrid ensemble learning algorithms and high-resolution satellite imageries," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 1-37, October.
    • Handle: RePEc:spr:nathaz:v:119:y:2023:i:1:d:10.1007_s11069-023-06106-7
    DOI: 10.1007/s11069-023-06106-7
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    References listed on IDEAS

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    1. Peyman Yariyan & Saeid Janizadeh & Tran Phong & Huu Duy Nguyen & Romulus Costache & Hiep Le & Binh Thai Pham & Biswajeet Pradhan & John P. Tiefenbacher, 2020. "Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 3037-3053, July.
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    1. Erfan Mahmoodi & Mahmood Azari & Mohammad Taghi Dastorani & Aryan Salvati, 2024. "Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5343-5363, October.

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