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Application of hybrid model-based deep learning and swarm‐based optimizers for flood susceptibility prediction in Binh Dinh province, Vietnam

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Abstract

Flooding is a serious natural hazard. It causes considerable impact on human life, the environment, and property, worldwide. Building a highly accurate flood susceptibility map can reduce disaster damage; it has become the main approach in flood risk management. The objective of this research is to build flood susceptibility maps for Binh Dinh province in Vietnam, applying modern machine learning and remote sensing methods, namely deep neural networks (DNN) and swarm-based optimization algorithms such as aquila optimizer algorithm (AO), sea lion optimization (SLnO), elephant herding optimization (EHO), the naked mole-rat algorithm (NMRA), Stochastic Gradient Descent (SGD). The geospatial distribution analysis approach was used to construct the input data, including 1883 sample points and 12 conditioning factors. Several well-known algorithms were used as reference models to compare the accuracy of each proposed model. The statistical indices root mean square error (RMSE), area under curve (AUC), mean absolute error (MAE), accuracy, and F1 score were used to validate the proposed model. The results show that five optimization algorithms successfully in buiding flood susceptibility maps and these models performed well with an AUC value of more than 0.97. The DNN-NMRA model came first (RMSE = 0.16, AUC = 0.99), followed by DNN-SLnO (RMSE = 0.39, AUC = 0.99), DNN-EHO (RMSE = 0.41, AUC = 0.99), DNN-AO (RMSE = 0.46, AUC = 0.97), and DNN-SGD (RMSE = 0.49, AUC = 0.93) respectively. Furthermore, the results show that the precision of new models (DNN-NMRA, DNN-SlnO, DNN-EHO, DNN-AO) surpassed the standard model DNN-SGD. The results of this study are useful in the construction of appropriate flood management strategies in at-risk regions.

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Data availability

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

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Authors and Affiliations

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

    Huu Duy Nguyen

  2. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Chien Pham Van

  3. Institude for Hydro Power and Renewable Energy, 95 Chua Boc, Dong Da, Hanoi, Vietnam

    Anh Duc Do

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  1. Huu Duy Nguyen
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  2. Chien Pham Van
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Huu Duy Nguyen, Chien Pham Van, Anh Duc Do. The first draft of the manuscript was written by Huu Duy Nguyen, Chien Pham Van, Anh Duc Do. All authors read and approved the final manuscript.

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Nguyen, H.D., Van, C.P. & Do, A.D. Application of hybrid model-based deep learning and swarm‐based optimizers for flood susceptibility prediction in Binh Dinh province, Vietnam. Earth Sci Inform 16, 1173–1193 (2023). https://doi.org/10.1007/s12145-023-00954-4

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