Using fuzzy and machine learning iterative optimized models to generate the flood susceptibility maps: case study of Prahova River basin, Romania

Abstract

In this work, the vulnerability to flooding in the Prahova River basin was calculated and analyzed using advanced methods and techniques. Thus, 2 hybrid models represented by Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) and Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) were generated, which had as input data the values of 10 flood predictors and a number of 158 points where historical floods occurred. In the first step, the Certainty Factor values were calculated, which were then used in the Fuzzy-Analytical Hierarchy Process and Multiclass Alternating Decision Tree models. It should be mentioned that the Multiclass Alternating Decision Tree model was optimized with the help of the Iterative Classifier Optimizer. In the case of both ensemble models the slope angle was the most important flood conditioning factor. Moreover, according to Certainty Factor modelling the 8 classes/categories achieved the maximum value of 1. Next, the susceptibility to floods on the surface of the study area was derived. On average, about 20% of the study area has areas with high and medium susceptibility to flash floods. After evaluating the quality of the models through Receiver Operating Characteristics (ROC) Curve, the following results emerged: Success Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.985) and Flood Potential Index (FPI) Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.967); Prediction Rate for Flood Potential Index (FPI) Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor (ICO-LADT-CF) (Area Under Curve = 0.952) and Flood Potential Index Fuzzy-Analytical Hierarchy Process – Certainty Factor (FAHP-CF) (Area Under Curve = 0.913). At the same time, the accuracies of the models were: Training dataset − 0.943 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.931 (Fuzzy-Analytical Hierarchy Process – Certainty Factor); Validating dataset − 0.935 (Iterative Classifier Optimizer – Multiclass Alternating Decision Tree – Certainty Factor) and 0.926 (Fuzzy-Analytical Hierarchy Process – Certainty Factor). As main conclusion, it can be mentioned that the 2 ensemble models outperform the previous machine learning models applied on the same study area before.

Keywords:

• Prahova river basin
• machine learning
• hybrid models
• flood susceptibility
• risk assessment

Acknowledgements

The authors are thankful to the editors and potential reviewers

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data will be made available upon request by the first author.

Compliance with ethical standards

Disclosure of potential conflicts of interest

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Availability of data

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

Authors contributions

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