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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Bibliographic Details
Published in:PloS one 2022-10, Vol.17 (10), p.e0273073
Main Authors: Jing, Zhanjie, Gao, Xiaohong
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0273073