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Citation |
Ruidas D, Saha A, Islam ARMT, Costache R, Pal SC. Environ. Sci. Pollut. Res. Int. 2022; ePub(ePub): ePub. |
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Copyright |
(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group) |
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DOI |
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PMID |
36229727 |
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Abstract |
The occurrences of flash floods in sub-tropical climatic regions like India are ubiquitous phenomena, particularly during the monsoon season. This type of flood occurs within a short period of time and makes it distinctive from all-natural hazards, which causes huge loss of economy and causalities of life. Therefore, its prediction is crucial and one of the challenging tasks for researchers to mitigate this sustainably. Furthermore, identifying flash flood susceptible regions is the foremost responsibility in managing flood events, which helps the local administration take emergency relief operations in flood-prone regions. In September 2021, the flood in the Gandheswari river basin was the most severe compared to the past decade. The occurrences of flash floods in the lower course of the Gandheswari river has been affected riparian habitats rigorously. Thus, in this study, we proposed the bivariate logistic regression (LR) method to delineate this river basin's flash flood hazard (FFH) map. Here, sixteen flood conditioning factors were selected for modeling purposes with the help of a multicollinearity test, and a total of 71 flood points were identified from the historical dataset. The produced result was validated by six distinctive validating techniques, including receiver operating characteristics (ROC) analysis, specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and F-score. These techniques have shown that present modeling has high predictive performance in both training and testing dataset with the values of ROC (training-0.928, validating-0.892), specificity (training-0.911, validating-0.882), sensitivity (training-0.915, validating-0.885), PPV (training-0.912, validating-0.874), NPV (training-0.91, validating-0.875), and F-score (training-0.92, validating-0.89). Therefore, the proposed method in this and the outcome result will help the disaster manager make proper decisions to mitigate the hazardous situation and take sustainable emergency relief operations. Language: en |
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Keywords |
Bivariate logistic regression; Emergency relief operation; Flash flood; Gandheswari River basin |