Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network |
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Authors: | Email author" target="_blank">Saro?LeeEmail author Inhye?Park Jong-Kuk?Choi |
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Institution: | (1) Geoscience Information Center, Korea Institute of Geoscience & Mineral Resources, (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea;(2) Department of Geoinformatics, University of Seoul, Siripdae-gil 13, Dongdaemun-gu, Seoul, 130-743, Republic of Korea;(3) Korea Ocean Satellite Centre, Korea Ocean Research & Development Institute, 454 Haean-no, Sangrok-gu, Ansan, Gyeonggi, 426-744, Korea |
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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 back-propagation 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. |
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