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Flash flood is one of the most devastating types of disasters. Roads are linear strip structures exposed to the natural environment and they are vulnerable to damage when flash flood occurs. Thus, an accurate assessment of flash flood vulnerability for roads is mandatory for the improvement of transportation flash flood forecasting and warnings. In this study, we constructed an assessment model for assessing the flash flood vulnerability of roads based on their exposure and disaster reduction capability. Then, considering the mechanism of flash floods acting on the road, we preliminarily established an index system with analytic hierarchy process (AHP), and used a support vector machine (SVM) to aggregate the indices. The use of SVM avoids the subjectivity of vulnerability evaluation. The Assessment results showed that 4.29% of the land area has high or extremely high vulnerability. The moderate vulnerability areas accounted for 5.19% of the land area. The low and extremely low vulnerability areas accounted for 15.81% of the land area; and the land area with no roads accounted for 43.94%. The suburban areas with dense roads in the southeast and the areas near the main roads in the northwest, had high and extremely high vulnerability. The low and extremely low vulnerability areas are mainly distributed in the areas with sparse roads and close to cities. The results of this study satisfy the need for the high precision and large-scale evaluation of flash flood vulnerability of roads. Based on the results of the vulnerability assessment, different disaster prevention and reduction measures should be taken in the northwest and southeast. The northwestern region should attach importance to the construction of roads and improve the disaster prevention and reduction system; while the southeastern region should plan layout and construction of new roads rationally and enhance the disaster resistance of existing roads. [ABSTRACT FROM AUTHOR]

Flood is one of the most serious natural disasters in the world. Flood losses in the developing countries throughout the Belt and Road region are more than twice the global average. However, to date, the extent of the vulnerability of the Belt and Road region remains poorly understood. Therefore, this study sought to address this knowledge gap. In this study, we presented a vulnerability assessment model based on triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and support vector machine (SVM) model. Firstly, a geospatial database including 11 flood conditioning factors was built. Secondly, the exposure and disaster reduction capability were calculated based on TFN-AHP and SVM, respectively. Finally, the spatial distribution of vulnerability throughout the Belt and Road region was generated. According to the results, the exposure and disaster reduction capability in most areas are extremely low, accounting for 86.45% and 80.53%, respectively. Meanwhile, the vulnerability of 47,105,300 km2 areas is low or extremely low, accounting for 93% of the Belt and Road region. The high-vulnerable areas (accounting for 3.54%) are primarily concentrated in the southern and eastern parts of China, northern India, most areas of Bangladesh, the Indus Valley in Pakistan, the Nile River Basin in Egypt, and the central region of Indonesia. Obviously, these regions with high vulnerability are characterized by frequent economic activities and dense populations. As suggested of these results, this study provides scientific and technological evidence for the prevention and mitigation of flood disasters in the countries along the Belt and Road region. [ABSTRACT FROM AUTHOR]

• Classification methods can influence the performance of integrated modeling. • Clustering algorithms may improve accuracy of integrated modeling. • Compared to NBC, ISOMax may make the hybrid models more excellent. Flash flood is one of the most severe natural disasters around the world, and has caused sizeable economic losses and countless death. Assessing flash flood susceptibility by hybrid models of statistical and machine learning methods is essential for flood mitigation strategies and disaster preparedness. Although classifying the flash flood conditioning factors becomes a crucial step before applying these hybrid models, their impact on the accuracy of integrated modeling is still unclear. Most previous studies used natural break classification (NBC) and quantile classification methods by default to conduct the classification, but more classification methods have not been tried. In this context, this study introduced three clustering algorithms of K-Means, Expectation Maximization, and ISOMaximum likelihood algorithm (ISOMax) into the classification of factors, and compared them to NBC and quantile classification. To test the impact of classification methods on integrated modeling, these classification results were applied into the construction of three hybrid models (i.e., the integrating of frequency ratio with support vector machines, random forest, and bayesian-regularization neural networks). Then, the accuracy of these hybrid models was evaluated by using ROC curves and statistical indicators. The classification results show that the clustering intervals in the same factor varied with classification algorithms. It can be found from the model performance evaluation results that different classification algorithms will lead to discrepancies in accuracy of integrated modeling. Compared to NBC, the ISOMax allows a better fitting and prediction ability of hybrid models in this study. The application of clustering algorithm provides a new perspective for improving the accuracy of integrated modeling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Flash floods are one of the natural disasters that threaten the lives of many people all over the world every year. Flash floods are significantly affected by the intensification of extreme climate events and interactions with exposed and vulnerable socio-economic systems impede regional development processes. Hence, it is important to estimate the loss due to flash floods before the disaster occurs. However, there are no comprehensive vulnerability assessment results for flash floods in China. Fortunately, the National Mountain Flood Disaster Investigation Project provided a foundation to develop this proposed assessment. In this study, an index system was established from the exposure and disaster reduction capability categories, and is based on analytic hierarchy process (AHP) methods. We evaluated flash flood vulnerability by adopting the support vector machine (SVM) model. Our results showed 439 counties with high and extremely high vulnerability (accounting for 10.5% of the land area and corresponding to approximately 100 million hectares (ha)), 571 counties with moderate vulnerability (accounting for 19.18% of the land area and corresponding to approximately 180 million ha), and 1128 counties with low and extremely low vulnerability (accounting for 39.43% of the land area and corresponding to approximately 370 million ha). The highly-vulnerable counties were mainly concentrated in the south and southeast regions of China, moderately-vulnerable counties were primarily concentrated in the central, northern, and southwestern regions of China, and low-vulnerability counties chiefly occurred in the northwest regions of China. Additionally, the results of the spatial autocorrelation suggested that the "High-High" values of spatial agglomeration areas mainly occurred in the Zhejiang, Fujian, Jiangxi, Hunan, Guangxi, Chongqing, and Beijing areas. On the basis of these results, our study can be used as a proposal for population and building distribution readjustments, and the management of flash floods in China. [ABSTRACT FROM AUTHOR]