Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network SCIE SCOPUS

Cited 64 time in WEB OF SCIENCE Cited 68 time in Scopus
Title
Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network
Author(s)
Lee, Saro; Park, Inhye; Choi, Jong-Kuk
KIOST Author(s)
Choi, Jong Kuk(최종국)
Publication Year
2012-02
Abstract
Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the backpropagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.
ISSN
0364-152X
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/3634
DOI
10.1007/s00267-011-9766-5
Bibliographic Citation
ENVIRONMENTAL MANAGEMENT, v.49, no.2, pp.347 - 358, 2012
Publisher
SPRINGER
Subject
CLASSIFICATION; PROVINCE
Keywords
GIS; Abandoned underground coal mine; Ground subsidence; Artificial neural networks; Korea
Type
Article
Language
English
Document Type
Article
Publisher
SPRINGER
Related Researcher
Research Interests

GOCI-based PAR,Satellite-based Water Quality,Ocean color based Ecological Environment,GOCI 기반 PAR,위성활용 해수표층 수질,해색위성 기반 해수 생태환경

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