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Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management

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  • Romulus Costache

    (Research Institute of the University of Bucharest
    National Institute of Hydrology and Water Management)

Abstract

In Romania, as in the rest of the world, the flood frequency has increased considerably. Prahova river basin is among the most exposed catchments of the country to flood risk. It also represents the area of the present study for which the identification of surfaces with high susceptibility to flood phenomena was attempted by applying 2 hybrid models (adaptive neuro-fuzzy inference system and fuzzy support vector machine hybrid) and 2 bivariate statistical models (certainty factor and statistical index). The computation of Flood Potential Index (FPI) was possible by considering a number of 10 flood conditioning factors together with a number of 158 flood pixels and 158 non-flood pixels. Generally, the high and very high flood potential appears on around 25% of the upper and middle basin of Prahova river. The validation of the results was made through the ROC Curve model. One of the novelties of this research is related to the application of Fuzzy Support Vector Machine ensemble for the first time in a study concerning the evaluation of the susceptibility to a certain natural hazard.

Suggested Citation

  • Romulus Costache, 2019. "Flood Susceptibility Assessment by Using Bivariate Statistics and Machine Learning Models - A Useful Tool for Flood Risk Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3239-3256, July.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02301-z
    DOI: 10.1007/s11269-019-02301-z
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    References listed on IDEAS

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    1. Chen Cao & Peihua Xu & Yihong Wang & Jianping Chen & Lianjing Zheng & Cencen Niu, 2016. "Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas," Sustainability, MDPI, vol. 8(9), pages 1-18, September.
    2. Omid Rahmati & Hamid Reza Pourghasemi, 2017. "Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(5), pages 1473-1487, March.
    3. Khabat Khosravi & Ebrahim Nohani & Edris Maroufinia & Hamid Reza Pourghasemi, 2016. "A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making techn," 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. 83(2), pages 947-987, September.
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