Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

Chen Cao, Peihua Xu, Yihong Wang, Jianping Chen, Lianjing Zheng and Cencen Niu
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Chen Cao: College of Construction Engineering of Jilin University, Changchun 130026, Jilin, China
Peihua Xu: College of Construction Engineering of Jilin University, Changchun 130026, Jilin, China
Yihong Wang: Beijing Institute of Geology, Beijing 100120, China
Jianping Chen: College of Construction Engineering of Jilin University, Changchun 130026, Jilin, China
Lianjing Zheng: College of Construction Engineering of Jilin University, Changchun 130026, Jilin, China
Cencen Niu: College of Construction Engineering of Jilin University, Changchun 130026, Jilin, China

Sustainability, 2016, vol. 8, issue 9, 1-18

Abstract: This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations ( n = 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent ( n = 60) of the flooding hazard locations were randomly selected for building the models. The remaining 30% ( n = 25) of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

Keywords: short-term heavy rain; subsidence risk area; flash flood hazard (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (22)

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