4.6 Article

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

期刊

SUSTAINABILITY
卷 8, 期 9, 页码 -

出版社

MDPI AG
DOI: 10.3390/su8090948

关键词

short-term heavy rain; subsidence risk area; flash flood hazard

资金

  1. State Key Program of National Natural Science of China [41330636]
  2. Natural Science Foundations of China [41402243]
  3. Beijing science and technology project [Z141100003614052]
  4. Graduate Innovation Fund of Jilin University [2016208]

向作者/读者索取更多资源

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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据