Paper Info
Title
Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping
Abstract
We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.
Year
DOI
Venue
2012
10.1016/j.cageo.2012.01.005
Computers & Geosciences
Keywords
Field
DocType
anfis model,ground subsidence map,resulting ground subsidence hazard,ground subsidence area,ground subsidence hazard map,ground subsidence hazard mapping,adaptive neuro-fuzzy inference system,ground subsidence,ground subsidence test data,adaptive anfis model,land use,gis
Data mining,Topographic map,Computer science,Coal mining,Remote sensing,Subsidence,Test data,Adaptive neuro fuzzy inference system,Geodesy,Spatial database,Inference system
Journal
Volume
ISSN
Citations 
48,
0098-3004
2
PageRank 
References 
Authors
0.40
4
4
Name
Order
Citations
PageRank
Inhye Park1163.61
Jaewon Choi29211.74
Moung Jin Lee363.86
Saro Lee49315.12