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

DC Field Value Language
dc.contributor.author Lee, Saro -
dc.contributor.author Park, Inhye -
dc.contributor.author Choi, Jong-Kuk -
dc.date.accessioned 2020-04-20T06:55:39Z -
dc.date.available 2020-04-20T06:55:39Z -
dc.date.created 2020-01-28 -
dc.date.issued 2012-02 -
dc.identifier.issn 0364-152X -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/3634 -
dc.description.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. -
dc.description.uri 1 -
dc.language English -
dc.publisher SPRINGER -
dc.subject CLASSIFICATION -
dc.subject PROVINCE -
dc.title Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network -
dc.type Article -
dc.citation.endPage 358 -
dc.citation.startPage 347 -
dc.citation.title ENVIRONMENTAL MANAGEMENT -
dc.citation.volume 49 -
dc.citation.number 2 -
dc.contributor.alternativeName 최종국 -
dc.identifier.bibliographicCitation ENVIRONMENTAL MANAGEMENT, v.49, no.2, pp.347 - 358 -
dc.identifier.doi 10.1007/s00267-011-9766-5 -
dc.identifier.scopusid 2-s2.0-84856977498 -
dc.identifier.wosid 000301300200006 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.subject.keywordPlus CLASSIFICATION -
dc.subject.keywordPlus PROVINCE -
dc.subject.keywordAuthor GIS -
dc.subject.keywordAuthor Abandoned underground coal mine -
dc.subject.keywordAuthor Ground subsidence -
dc.subject.keywordAuthor Artificial neural networks -
dc.subject.keywordAuthor Korea -
dc.relation.journalWebOfScienceCategory Environmental Sciences -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.relation.journalResearchArea Environmental Sciences & Ecology -
Appears in Collections:
Marine Digital Resources Department > Korea Ocean Satellite Center > 1. Journal Articles
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