Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

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.

Performance Evaluation of Five GIS-Based Models for Landslide Susceptibility Prediction and Mapping: A Case Study of Kaiyang County, China

“…Topographic relief can reflect the macroscopic features of the terrain within a certain range [47]. Based on 30 m resolution DEM data, this study calculated the topographic relief of the study area and divided it into five classes (<18, 18-32, 32-50, 50-79, and >79 m) from Jenks natural breaks method (Figure 3c).…”

“…To consider the weight of each category of indicators and the weight of indicators at the same time, Cao (2020) [47] coupled the CF with LR and proposed the CF-LR integrated model. First, the CF value of each grade of evaluation factors calculated by the CF model was used as the index value of the LR analysis.…”

“…Binomial LR analysis was performed to obtain the regression coefficient of each impact factor. On this basis, a LR equation was established to evaluate the landslide susceptibility [47].…”

“…Each model has its own characteristics and shortcomings, making it difficult to objectively and accurately evaluate landslide susceptibility [45]. The analytic hierarchy process (AHP), CF, and logistic regression (LR) have been applied in a large number of evaluations because of their simplicity, strong operability, and evaluation effect, but each has its own limitations [46,47]. The CF model can solve the evaluation factors based on the impact of different internal eigenvalues on the degree of liability sensitivity and by ignoring the differences in the impact of various factors on liability.…”

“…The AHP cannot consider each factor's influence on different characteristic variables [46]. The LR model cannot solve the quantitative problem of each influence factor, especially in terms of consolidation issues associated with multi-source data [47]. Owing to these shortcomings, regional landslides are difficult to evaluate objectively and quantitatively.…”