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Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning

Author

Listed:
  • Izhar Ahmad

    (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)

  • Rashid Farooq

    (International Islamic University
    Swinburne University of Technology)

  • Muhammad Ashraf

    (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)

  • Muhammad Waseem

    (Ghulam Ishaq Khan Institute of Engineering Sciences and Technology)

  • Donghui Shangguan

    (Chinese Academy of Sciences
    CAS-HEC
    University of Chinese Academy of Sciences)

Abstract

Floods are natural disasters with significant economic and infrastructural impacts. Assessing flood susceptibility in mountainous urban regions is particularly challenging due to the complicated interaction in which urban structures and mountainous terrain affect flood behavior. This study employs two ensemble machine learning algorithms, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to develop flood susceptibility maps for the Hunza-Nagar region, which has been experiencing frequent flooding for the past three decades. An unsteady flow simulation is carried out in HEC-RAS utilizing a 100-year return period flood hydrograph as an input boundary condition, the output of which provided the spatial inundation extents necessary for developing the flood inventory. Ten explanatory factors, including climatic, geological, and geomorphological features namely elevation, slope, curvature, topographic wetness index (TWI), normalized difference vegetation index (NDVI), land use land cover (LULC), rainfall, lithology, distance to roads and distance to rivers are considered for the flood susceptibility mapping. For developing flood inventory, random sampling technique is adopted to create a spatial repository of flood and non-flood points, incorporating the ten geo-environmental flood conditioning factors. The models’ accuracy is assessed using the area under the curve (AUC) of receiver operating characteristics (ROC). The prediction rate AUC values are 0.912 for RF and 0.893 for XGBoost, with RF also demonstrating superior performance in accuracy, precision, recall, F1-score, and kappa evaluation metrics. Consequently, the RF model is selected to represent the flood susceptibility map for the study area. The resulting flood susceptibility maps will assist national disaster management and infrastructure development authorities in identifying high flood susceptible zones and carrying out early mitigation actions for future floods.

Suggested Citation

  • Izhar Ahmad & Rashid Farooq & Muhammad Ashraf & Muhammad Waseem & Donghui Shangguan, 2025. "Improving flood hazard susceptibility assessment by integrating hydrodynamic modeling with remote sensing and ensemble machine learning," 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. 121(7), pages 7839-7868, April.
  • Handle: RePEc:spr:nathaz:v:121:y:2025:i:7:d:10.1007_s11069-025-07109-2
    DOI: 10.1007/s11069-025-07109-2
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    References listed on IDEAS

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    1. P. V. Timbadiya & K. M. Krishnamraju, 2023. "A 2D hydrodynamic model for river flood prediction in a coastal floodplain," 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. 115(2), pages 1143-1165, January.
    2. Shihai Wu & Yili Zhang & Jianzhong Yan, 2022. "Comprehensive Assessment of Geopolitical Risk in the Himalayan Region Based on the Grid Scale," Sustainability, MDPI, vol. 14(15), pages 1-20, August.
    3. Md Golam Rabbani Fahad & Rouzbeh Nazari & M. H. Motamedi & Maryam E. Karimi, 2020. "Coupled Hydrodynamic and Geospatial Model for Assessing Resiliency of Coastal Structures under Extreme Storm Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1123-1138, February.
    4. Muhammad Sajjad & Zulfiqar Ali & Mirza Waleed, 2023. "Has Pakistan learned from disasters over the decades? Dynamic resilience insights based on catastrophe progression and geo-information models," 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. 117(3), pages 3021-3042, July.
    5. Ping Zhang & Yiqiao Jia & Youlin Shang, 2022. "Research and application of XGBoost in imbalanced data," International Journal of Distributed Sensor Networks, , vol. 18(6), pages 15501329221, June.
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