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Monitoring and early warning of a metal mine tailings pond based on a deep learning bidirectional recurrent long and short memory network
The effective monitoring and early warning capability of metal mine tailings ponds can improve the associated safety risk management level. The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond monitoring and early warning system, which provides...
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| Published in: | PloS one 2022-10, Vol.17 (10), p.e0273073 |
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| description | The effective monitoring and early warning capability of metal mine tailings ponds can improve the associated safety risk management level. The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond monitoring and early warning system, which provides technical support for the design and daily management of tailings reservoir early warning systems, is constructed. Based on a deep learning bidirectional recurrent long and short memory network, an infiltration line prediction model with univariate input and an infiltration line prediction model with multivariate input are proposed. The data adopted are those from four monitoring points of the same cross-section at different positions and data from one adjacent internal lateral displacement and internal vertical displacement monitoring point. Using the adaptive moment estimation (Adam) optimization algorithm and the root mean square error (RMSE) model evaluation metric, the multilayer perceptron model, univariate input model, and multivariate input model are compared. This work shows that their RMSEs are 0.10611, 0.09966, and 0.11955, respectively. |
| doi_str_mv | 10.1371/journal.pone.0273073 |
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The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond monitoring and early warning system, which provides technical support for the design and daily management of tailings reservoir early warning systems, is constructed. Based on a deep learning bidirectional recurrent long and short memory network, an infiltration line prediction model with univariate input and an infiltration line prediction model with multivariate input are proposed. The data adopted are those from four monitoring points of the same cross-section at different positions and data from one adjacent internal lateral displacement and internal vertical displacement monitoring point. Using the adaptive moment estimation (Adam) optimization algorithm and the root mean square error (RMSE) model evaluation metric, the multilayer perceptron model, univariate input model, and multivariate input model are compared. This work shows that their RMSEs are 0.10611, 0.09966, and 0.11955, respectively.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0273073</identifier><identifier>PMID: 36227890</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Analysis ; Artificial intelligence ; Biology and Life Sciences ; Computational linguistics ; Computer and Information Sciences ; Data processing ; Deep Learning ; Early warning systems ; Earth Sciences ; Earthquakes ; Emergency communications systems ; Environmental Monitoring ; Failure ; Fault diagnosis ; Infiltration ; Language processing ; Lateral displacement ; Machine learning ; Metals ; Mine tailings ; Mine wastes ; Mineral resources ; Modelling ; Monitoring ; Multilayer perceptrons ; Multivariate analysis ; Natural language interfaces ; Neural networks ; Optimization ; Physical Sciences ; Ponds ; Prediction models ; Rain ; Research and Analysis Methods ; Reservoir management ; Reservoirs ; Risk management ; Root-mean-square errors ; Safety management ; Shear strength ; Storm damage ; Tailings ; Technical services ; Time series ; Warning systems</subject><ispartof>PloS one, 2022-10, Vol.17 (10), p.e0273073</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Jing, Gao. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond monitoring and early warning system, which provides technical support for the design and daily management of tailings reservoir early warning systems, is constructed. Based on a deep learning bidirectional recurrent long and short memory network, an infiltration line prediction model with univariate input and an infiltration line prediction model with multivariate input are proposed. The data adopted are those from four monitoring points of the same cross-section at different positions and data from one adjacent internal lateral displacement and internal vertical displacement monitoring point. Using the adaptive moment estimation (Adam) optimization algorithm and the root mean square error (RMSE) model evaluation metric, the multilayer perceptron model, univariate input model, and multivariate input model are compared. This work shows that their RMSEs are 0.10611, 0.09966, and 0.11955, respectively.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Artificial intelligence</subject><subject>Biology and Life Sciences</subject><subject>Computational linguistics</subject><subject>Computer and Information Sciences</subject><subject>Data processing</subject><subject>Deep Learning</subject><subject>Early warning systems</subject><subject>Earth Sciences</subject><subject>Earthquakes</subject><subject>Emergency communications systems</subject><subject>Environmental Monitoring</subject><subject>Failure</subject><subject>Fault diagnosis</subject><subject>Infiltration</subject><subject>Language processing</subject><subject>Lateral displacement</subject><subject>Machine learning</subject><subject>Metals</subject><subject>Mine tailings</subject><subject>Mine wastes</subject><subject>Mineral resources</subject><subject>Modelling</subject><subject>Monitoring</subject><subject>Multilayer 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warning of a metal mine tailings pond based on a deep learning bidirectional recurrent long and short memory network</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-10-13</date><risdate>2022</risdate><volume>17</volume><issue>10</issue><spage>e0273073</spage><pages>e0273073-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The effective monitoring and early warning capability of metal mine tailings ponds can improve the associated safety risk management level. The infiltration line is an important core index of tailings pond stability. In this paper, a tailings pond monitoring and early warning system, which provides technical support for the design and daily management of tailings reservoir early warning systems, is constructed. Based on a deep learning bidirectional recurrent long and short memory network, an infiltration line prediction model with univariate input and an infiltration line prediction model with multivariate input are proposed. The data adopted are those from four monitoring points of the same cross-section at different positions and data from one adjacent internal lateral displacement and internal vertical displacement monitoring point. Using the adaptive moment estimation (Adam) optimization algorithm and the root mean square error (RMSE) model evaluation metric, the multilayer perceptron model, univariate input model, and multivariate input model are compared. This work shows that their RMSEs are 0.10611, 0.09966, and 0.11955, respectively.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36227890</pmid><doi>10.1371/journal.pone.0273073</doi><tpages>e0273073</tpages><orcidid>https://orcid.org/0000-0003-0288-4295</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Artificial intelligence Biology and Life Sciences Computational linguistics Computer and Information Sciences Data processing Deep Learning Early warning systems Earth Sciences Earthquakes Emergency communications systems Environmental Monitoring Failure Fault diagnosis Infiltration Language processing Lateral displacement Machine learning Metals Mine tailings Mine wastes Mineral resources Modelling Monitoring Multilayer perceptrons Multivariate analysis Natural language interfaces Neural networks Optimization Physical Sciences Ponds Prediction models Rain Research and Analysis Methods Reservoir management Reservoirs Risk management Root-mean-square errors Safety management Shear strength Storm damage Tailings Technical services Time series Warning systems |
| title | Monitoring and early warning of a metal mine tailings pond based on a deep learning bidirectional recurrent long and short memory network |
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