Flash-Flood Potential Index estimation using Fuzzy Logic combined with Deep Learning Neural Network, Naïve Bayes, XGBoost and Classification and Regression Tree

Abstract

Flash floods pose a major challenge in various regions of the world, causing serious damage to life and property. Here we investigated the Izvorul Dorului river basin from Romania, to identify slope surfaces with a high potential for flash-flood employing a combination of Fuzzy Logic algorithm with the following four machine learning models: Classification and Regression Tree, Deep Learning Neural Network, XGBoost, and Naïve Bayes. Ten flash-flood predictors were used as independent variables to determine the flash-flood potential index. As a dependent variable, we used areas with ttorrential phenomena divided into training (70%) and validating data set (30%). Predictive ability and the degree of Correlation between factors were assessed through the Correlation-based Feature Selection (CFS) method and through the Confusion Matrix, respectively. In the training phase, all ensemble models yielded good and very good accuracies of over 84%. The spatialization of Flash-Flood Potential Index (FFPI) over the study area showed that high and very high values of flash-flood potential occur in the northern half of the region and occupy the following weights within the study area: 53.11% (FFPI Fuzzy-CART), 45.09% (Fuzzy-DLNN), 45.58% (Fuzzy-NB) and 44.85% (Fuzzy-XGBoost). The validation of the results was done through the ROC Curve method. Thus, according to Success Rate, Fuzzy-XGBoost (AUC =0.886) is the best model, while in terms of Prediction Rate, the ideal one is Fuzzy-DLNN (AUC =0.84). The novelty of this work is the application of the four ensemble models in evaluating this natural hazard.