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Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm

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Abstract

In regions with limited flow and catchment data needed for the configuration and calibration of hydraulic and hydrological models, employing spatial flood modeling and mapping enables authorities to predict the spatial extent and severity of floods. This study leveraged flood inventory data, coupled with various conditional variables, to formulate a novel Ensemble model. This ensemble model combined four hybridized models based on Support Vector Machine (SVM), Naïve Bayes (NB), Decision Classification Tree (DCT), and Artificial Neural Network (ANN), all of which were optimized using the metaheuristic Symbiotic Organisms Search algorithm (SOS). The precision of the flood inundation map generated by the four hybrid models and the ensemble model was assessed using standard metrics. The results demonstrated that the ensemble model outperformed other models, with an accuracy metric of 0.99 Area Under the Curve (AUC) during the training stage and 0.96 during the testing stage. This underscores the effectiveness of the ensemble approach in flood preparedness and response applications. Furthermore, a comparison was conducted, comparing the performance of the developed ensemble model against other studies within the state of West Bengal. The findings highlighted a significant improvement in the ensemble model's performance with an AUC score of 0.96 in validation compared to studies in similar areas within West Bengal with AUC score ranged from 0.73 to 0.92. In conclusion, the methodology employed in this study holds promise for application in other regions worldwide that face challenges related to limited data availability for accurate flood inundation mapping.

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Data availability

The data that support the findings of this study are available on request from the corresponding author.

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Funding

No external funding is received in this study.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur, 713209, India

    Gilbert Hinge, Amandeep Kumar & Ruchika Gupta

  2. Department of Civil Engineering, Nirma University, Ahmedabad, India

    Swati Sirsant

  3. Department of Civil and Environmental Engineering, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates

    Mohamed A. Hamouda

  4. National Water and Energy Centre, United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates

    Mohamed A. Hamouda

Authors
  1. Gilbert Hinge
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  2. Swati Sirsant
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  3. Amandeep Kumar
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  4. Ruchika Gupta
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  5. Mohamed A. Hamouda
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Contributions

Conceptualization, G.H., M.A.H. and A.K.; Data curation, G.H., and R.G; Formal analysis, G.H., A.K.; Investigation, G.H, A.K, R.G; Methodology, G.H, S.S and M.A.H; Project administration, M.A.H., and G.H; Resources, M.A.H.; Supervision, M.A.H., G.H. and S.S; Visualization, G.H., R.G. and A.K.; Writing—original draft, G.H. and S.S; Review and editing, M.A.H. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Mohamed A. Hamouda.

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Hinge, G., Sirsant, S., Kumar, A. et al. Enhancing flood prediction in Southern West Bengal, India using ensemble machine learning models optimized with symbiotic organisms search algorithm. Stoch Environ Res Risk Assess (2024). https://doi.org/10.1007/s00477-024-02712-4

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