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Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling

Author

Listed:
  • Saeid Janizadeh

    (Tarbiat Modares University)

  • Mehdi Vafakhah

    (Tarbiat Modares University)

  • Zoran Kapelan

    (Delft University of Technology)

  • Naghmeh Mobarghaee Dinan

    (Environmental Sciences Research Institute, Shahid Beheshti University)

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.

Suggested Citation

  • Saeid Janizadeh & Mehdi Vafakhah & Zoran Kapelan & Naghmeh Mobarghaee Dinan, 2021. "Novel Bayesian Additive Regression Tree Methodology for Flood Susceptibility Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4621-4646, October.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:13:d:10.1007_s11269-021-02972-7
    DOI: 10.1007/s11269-021-02972-7
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    References listed on IDEAS

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    1. Michelle Woodward & Zoran Kapelan & Ben Gouldby, 2014. "Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 75-92, January.
    2. G. Papaioannou & L. Vasiliades & A. Loukas, 2015. "Multi-Criteria Analysis Framework for Potential Flood Prone Areas Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 399-418, January.
    3. Saeid Janizadeh & Mohammadtaghi Avand & Abolfazl Jaafari & Tran Van Phong & Mahmoud Bayat & Ebrahim Ahmadisharaf & Indra Prakash & Binh Thai Pham & Saro Lee, 2019. "Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    4. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
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    Cited by:

    1. Romulus Costache & Alireza Arabameri & Iulia Costache & Anca Crăciun & Binh Thai Pham, 2022. "New Machine Learning Ensemble for Flood Susceptibility Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4765-4783, September.
    2. Sina Paryani & Mojgan Bordbar & Changhyun Jun & Mahdi Panahi & Sayed M. Bateni & Christopher M. U. Neale & Hamidreza Moeini & Saro Lee, 2023. "Hybrid-based approaches for the flood susceptibility prediction of Kermanshah province, Iran," 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. 116(1), pages 837-868, March.
    3. Maia, Mateus & Murphy, Keefe & Parnell, Andrew C., 2024. "GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    4. Qiang Hu & Yuelong Zhu & Hexuan Hu & Zhuang Guan & Zeyu Qian & Aiming Yang, 2022. "Multiple Kernel Learning with Maximum Inundation Extent from MODIS Imagery for Spatial Prediction of Flood Susceptibility," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 55-73, January.

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