Abstract
Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.
Highlights
Flash floods arise from interactions between the hydrological and the atmospheric systems
As with all bivariate statistical models, the provides a good represemble models to determine flood susceptibility in this study: support vector machine (SVM)-fuzzy membership value (FMV), classification and regression trees (CART)-FMV, and sentation ofAs the relationships between the flood conditioning the flood occurCNN-FMV
We proposed three proposed hybrid models (SVM-FMV, CART-FMV, and convolutional neural network (CNN)-FMV) to identify the areas prone to flash floods within the Dadu
Summary
Flash floods arise from interactions between the hydrological and the atmospheric systems. They are characterized by a runoff peak developing over a period of minutes to hours during or after heavy rainfall, and they generally occur in the river basins smaller than 200 km2 [1]. They are considered to be one of the most devastating and frequent natural disasters worldwide [2]. ~4.63 million km of China is susceptible to flash floods, which have threatened 560 million people [4].
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