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Open AccessArticle

Application of Artificial Neural Networks in Assessing Mining Subsidence Risk

Department of Earth Resources and Environmental Engineering, Hanyang University, Seoul 04763, Korea
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Appl. Sci. 2020, 10(4), 1302; https://doi.org/10.3390/app10041302
Received: 6 December 2019 / Revised: 30 January 2020 / Accepted: 11 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Land Subsidence: Monitoring, Prediction and Modeling)
Subsidence at abandoned mines sometimes causes destruction of local areas and casualties. This paper proposes a mine subsidence risk index and establishes a subsidence risk grade based on two separate analyses of A and B to predict the occurrence of subsidence at an abandoned mine. For the analyses, 227 locations were ultimately selected at 15 abandoned coal mines and 22 abandoned mines of other types (i.e., gold, silver, and metal mines). Analysis A predicts whether subsidence is likely using an artificial neural network. Analysis B assesses a mine subsidence risk index that indicates the extent of risk of subsidence. Results of both analyses are utilized to assign a subsidence risk grade to each ground location investigated. To check the model’s reliability, a new dataset of 22 locations was selected from five other abandoned mines; the subsidence risk grade results were compared with those of the actual ground conditions. The resulting correct prediction percentage for 13 subsidence locations of the abandoned mines was 83–86%. To improve reliability of the subsidence risk, much more subsidence data with greater variations in ground conditions is required, and various types of analyses by numerical and empirical approaches, etc. need to be combined. View Full-Text
Keywords: mine subsidence; artificial neural networks; mine subsidence risk index; subsidence risk grade mine subsidence; artificial neural networks; mine subsidence risk index; subsidence risk grade
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MDPI and ACS Style

Kim, Y.; Lee, S.S. Application of Artificial Neural Networks in Assessing Mining Subsidence Risk. Appl. Sci. 2020, 10, 1302.

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