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New Machine Learning Ensemble for Flood Susceptibility Estimation

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Abstract

Floods are among the most severe natural hazard phenomena that affect people around the world. Due to this fact, the identification of zones highly susceptible to floods became a very important activity in the researcher’s work. In this context, the present research work aimed to propose the following 3 novel ensembles to estimate the flood susceptibility in Putna river basin from Romania: UltraBoost-Weights of Evidence (U-WOE), Stochastic Gradient Descending-Weights of Evidence (SGD-WOE) and Cost Sensitive Forest-Weights of Evidence (CSForest-WOE). In this regard, a sample of 132 flood locations and 14 flood predictors was used as input datasets in the 3 aforementioned models. The modeling procedure performed through a ten-fold cross-validation method revealed that the SGD-WOE ensemble model achieved the highest performance in terms of ROC Curve-AUC (0.953) and also in terms of Accuracy (0.94). The slope and distance from river flood predictors achieved the highest importance in terms of flood susceptibility genesis, while the aspect, TPI, hydrological soil groups, and plan curvature have the lowest influence in terms of flood occurrence. The area with high and very high susceptibility represents between 21% and 24% of the Putna river basin from Romania.

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Availability of Data and Materials

The data used in this research are available by the corresponding author upon reasonable request.

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Funding

This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS – UEFISCDI, project number PN-III-P1-1.1-PD-2019-0424-P, within PNCDI III.

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Authors and Affiliations

  1. Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania

    Romulus Costache

  2. Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania

    Romulus Costache & Anca Crăciun

  3. Department of Geomorphology, Tarbiat Modares University, 36581-17994, Tehran, Iran

    Alireza Arabameri

  4. Faculty of Geography, University of Bucharest, 010041, Bucharest, Romania

    Iulia Costache

  5. University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, 100000, Ha Noi, Vietnam

    Binh Thai Pham

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  1. Romulus Costache
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  2. Alireza Arabameri
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Contributions

Conceptualization, R.C., I.C., A.C. and B.T.P.; methodology, R.C., A.A. and I.C.; software, R.C., I.C., and B.T.P.; validation, R.C., I.C., A.C. and B.T.P.; formal analysis, R.C., A.A., A.C. and I.C.; investigation, R.C., A.A. and I.C.; resources, R.C., I.C. and B.T.P.; data curation, R.C., I.C. and B.T.P.; writing—original draft preparation, R.C., I.C. and B.T.P.; writing—review and editing, R.C., A.A. and I.C.; visualization, R.C. and B.T.P.; supervision, R.C., A.A. and I.C.; project administration, R.C.; funding acquisition, R.C. and B.T.P. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Alireza Arabameri.

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Costache, R., Arabameri, A., Costache, I. et al. New Machine Learning Ensemble for Flood Susceptibility Estimation. Water Resour Manage 36, 4765–4783 (2022). https://doi.org/10.1007/s11269-022-03276-0

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