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Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS

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Abstract

Floods are arguably the most impactful of natural hazards. The increasing magnitude of their effects on the environment, human life, and economic activities calls for improved management of water resources. Flood susceptibility modeling has been used around the world to reduce the damage caused by flooding, although the extrapolation problem still presents a significant challenge. This study develops a machine learning (ML) model utilizing deep neural network (DNN) and optimization algorithms, namely earthworm optimization algorithm (EOA), wildebeest herd optimization (WHO), biogeography-based optimization (BBO), satin bowerbird optimizer (SBO), grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO), to solve the extrapolation problem in the construction of flood susceptibility models. Quang Nam Province was chosen as a case study as it is subject to the significant impact of intense flooding, and Nghe An Province was selected as the region for extrapolation of the flood susceptibility model. Root mean square error (RMSE), receiver operating characteristic (ROC), the area under the ROC curve (AUC), and accuracy (ACC) were applied to assess and compare the fit of each of the models. The results indicated that the models in this study are a good fit in establishing flood susceptibility maps, all with AUC > 0.9. The deep neural network (DNN)-BBO model enjoyed the best results (AUC = 0.99), followed by DNN-WHO (AUC = 0.99), DNN-SBO (AUC = 0.98), DNN-EOA (AUC = 0.96), DNN-GOA (AUC = 0.95), and finally, DNN-PSO (AUC = 0.92). In addition, the models successfully solved the extrapolation problem. These new models can modify their behavior to evaluate flood susceptibility in different regions of the world. The models in this study distribute a first point of reference for debate on the solution to the extrapolation problem, which can support urban planners and other decision-makers in other coastal regions in Vietnam and other countries.

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Data availability

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

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Funding

Huu Duy Nguyen was funded by the Postdoctoral Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.STS.24.

Author information

Authors and Affiliations

  1. Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

    Huu Duy Nguyen, Quoc-Huy Nguyen & Quang-Thanh Bui

Authors
  1. Huu Duy Nguyen
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  2. Quoc-Huy Nguyen
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  3. Quang-Thanh Bui
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Contributions

Huu Duy Nguyen: conceptualization, methodology, material preparation, validation, analysis, writing of original draft, and writing including review and editing. Quang-Thanh Bui: conceptualization, methodology, material preparation, and writing including review and editing. Quoc-Huy Nguyen: conceptualization, methodology, material preparation, and data collection. All authors read and approved the final manuscript.

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Correspondence to Huu Duy Nguyen.

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Nguyen, H.D., Nguyen, QH. & Bui, QT. Solving the spatial extrapolation problem in flood susceptibility using hybrid machine learning, remote sensing, and GIS. Environ Sci Pollut Res 31, 18701–18722 (2024). https://doi.org/10.1007/s11356-024-32163-x

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  • DOI: https://doi.org/10.1007/s11356-024-32163-x

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