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Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

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Abstract

Identifying areas prone to flooding is a key step in flood risk management. The purpose of this study is to develop and present a novel flood susceptibility model based on Bayesian Additive Regression Tree (BART) methodology. The predictive performance of the new model is assessed via comparison with the Naïve Bayes (NB) and Random Forest (RF) based methods that were previously published in the literature. All models were tested on a real case study based in the Kan watershed in Iran. The following fifteen climatic and geo-environmental variables were used as inputs into all flood susceptibility models: altitude, aspect, slope, plan curvature, profile curvature, drainage density, distance from river distance from road, stream power index (SPI), topographic wetness index (TPI), topographic position index (TPI), curve number (CN), land use, lithology and rainfall. Based on the existing flood field survey and other information available for the analyzed area, a total of 118 flood locations were identified as potentially prone to flooding. The data available were divided into two groups with 70% used for training and 30% for validation of all models. The receiver operating characteristic (ROC) curve parameters were used to evaluate the predictive accuracy of the new and existing models. Based on the area under curve (AUC) the new BART (86%) model outperformed the NB (80%) and RF (85%) models. Regarding the importance of input variables, the results obtained showed that the location’s altitude and distance from the river are the most important variables for assessing flooding susceptibility.

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Acknowledgements

We acknowledge Tarbiat Modares University's support for this work.

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The authors received no specific funding for this work.

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Authors and Affiliations

  1. Department of Watershed Management Engineering and Sciences, Faculty of Natural Resources and Marine Science, Tarbiat Modares University, Tehran, 14115-111, Iran

    Saeid Janizadeh & Mehdi Vafakhah

  2. Department of Water Management, Delft University of Technology, Delft, The Netherlands

    Zoran Kapelan

  3. Department of Environmental Planning and Design, Environmental Sciences Research Institute, Shahid Beheshti University, 1983969411, Tehran, Iran

    Naghmeh Mobarghaee Dinan

Authors
  1. Saeid Janizadeh
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  2. Mehdi Vafakhah
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  3. Zoran Kapelan
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  4. Naghmeh Mobarghaee Dinan
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Contributions

Saeid Janizadeh acquired the data; Saeid Janizadeh and Mehdi Vafakhah conceptualized and performed the analysis; Saeid Janizadeh wrote the manuscript and discussion, and analyzed the data; Mehdi Vafakhah, Zoran Kapelan and Naghmeh Mobarghaee Dinan provided technical sights, as well as edited, restructured, and professionally optimized the manuscript. All authors discussed the results and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Mehdi Vafakhah.

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Janizadeh, S., Vafakhah, M., Kapelan, Z. et al. Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling. Water Resour Manage 35, 4621–4646 (2021). https://doi.org/10.1007/s11269-021-02972-7

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