Quantitative determination of locations vulnerable to ground subsidence at mining regions is necessary for effective prevention. In this paper, a method of constructing subsidence susceptibility maps based on fuzzy relations is proposed and tested at an abandoned underground coal mine in Korea. An advantage of fuzzy combination operators over other methods is that the operation is mathematically and logically easy to understand and its implementation to GIS software is simple and straightforward. A certainty factor analysis was used for estimating the relative weight of eight major factors influencing ground subsidence. The relative weight of each factor was then converted into a fuzzy membership value and integrated as a subsidence hazard index using fuzzy combination operators, which produced coal mine subsidence susceptibility maps. The susceptibility maps were compared with the reported ground subsidence areas, and the results showed high accuracy between our prediction and the actual subsidence. Based on the root mean square error and accuracy in terms of success rates, fuzzy γ-operator with a low γ value and fuzzy algebraic product operator, specifically, are useful for ground subsidence prediction. Comparing the results of a fuzzy γ-operator and a conventional logistic regression model, the performance of the fuzzy approach is comparative to that of a logistic regression model with improved computational. A field survey done in the area supported the method's reliability. A combination of certainty factor analysis and fuzzy relations with a GIS is an effective method to determine locations vulnerable to coal mine subsidence.
Bibliographical noteFunding Information:
Acknowledgments This research was supported by NSL (National Space Lab) program through the Korea Science and Engineering Foundation funded by the Ministry of Education, Science and Technology (M105DA000004-08D0100-0011A). The authors are grateful to the staff of the Mine Reclamation Corporation, who provided investigation reports and a basic GIS database.
All Science Journal Classification (ASJC) codes
- Global and Planetary Change
- Environmental Chemistry
- Water Science and Technology
- Soil Science
- Earth-Surface Processes