Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Issue 23 (2nd December 2022)

• Record Type: Journal Article
• Authors: Costache, Romulus; Arabameri, Alireza; Moayedi, Hossein; Pham, Quoc Bao; Santosh, M.; Nguyen, Hoang; Pandey, Manish; Pham, Binh Thai
• Is Part Of: Geocarto international. Volume 37:Issue 23(2023)
• Page Start: 6780
• Page End: 6807
• Publication Date: 2022-12-02
• Subjects: Flash-flood potential index -- machine learning -- fuzzy logic -- ensemble models -- Romania; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1948109
• Languages: English
• ISSNs: 1010-6049

Abstract

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.