Abstract
Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.
Highlights
(2) The area under the curve (AUC) results showed that the certainty factor (CF)-logistic regression (LR) model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882)
Flash floods are a type of natural disaster that often occurs in mountainous areas and results in tremendous damage to infrastructure, human lives, and property [1]
We evaluated the performances of the three models
Summary
Flash floods are a type of natural disaster that often occurs in mountainous areas and results in tremendous damage to infrastructure, human lives, and property [1]. A statistic from the National Mountain Flood Disaster Investigation Project shows that China experienced more than 10,000 flash floods between 2010 and 2016, which led to the disappearance or death of at least 4800 people [3]. Fujian is considered to be the province that has experienced the most severe flash flood disasters in China; about 95% of the entire regions and 84% of the population are directly threatened by flash floods [4]. Examples of some recent flash floods in Fujian include Yongtai County in 2003, Liancheng County in 2010, and Changle City in 2015. The flash flood in Yongtai County caused more than 14 million USD worth of damage to roads, buildings, and agricultural land, while the flash flood in Liancheng city affected about 42.3 thousand people and led to the rapid relocation of 14.2 thousand people. The recognition and evaluation of regions susceptible to flash floods in Fujian Province are necessary and urgently needed to prevent and alleviate the damage and loss caused by flash floods
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