Evaluation of mine water quality based on the PCA–PSO–BP model
To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IWA Publishing
2024-02-01
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| Series: | Journal of Water and Climate Change |
| Subjects: | |
| Online Access: | http://jwcc.iwaponline.com/content/15/2/593 |
| _version_ | 1827278862269022208 |
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| author | Jiaqi Wang Yanli Huang |
| author_facet | Jiaqi Wang Yanli Huang |
| author_sort | Jiaqi Wang |
| collection | DOAJ |
| description | To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.
HIGHLIGHTS
Intelligent algorithms and neural networks are introduced into mine water quality evaluation.;
Established a PCA–PSO–BP model for mine water quality evaluation.;
Realized the accurate evaluation and reasonable prediction against the background of big data.;
Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.; |
| first_indexed | 2024-04-24T08:08:40Z |
| format | Article |
| id | doaj.art-c56f48a54d9c441f8246dd98812bb2ed |
| institution | Directory Open Access Journal |
| issn | 2040-2244 2408-9354 |
| language | English |
| last_indexed | 2024-04-24T08:08:40Z |
| publishDate | 2024-02-01 |
| publisher | IWA Publishing |
| record_format | Article |
| series | Journal of Water and Climate Change |
| spelling | doaj.art-c56f48a54d9c441f8246dd98812bb2ed2024-04-17T08:46:15ZengIWA PublishingJournal of Water and Climate Change2040-22442408-93542024-02-0115259360610.2166/wcc.2023.604604Evaluation of mine water quality based on the PCA–PSO–BP modelJiaqi Wang0Yanli Huang1 State Key Laboratory of Coal Resources and Safe Mining, School of Mines, China University of Mining & Technology, Xuzhou 221116, China State Key Laboratory of Coal Resources and Safe Mining, School of Mines, China University of Mining & Technology, Xuzhou 221116, China To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry. HIGHLIGHTS Intelligent algorithms and neural networks are introduced into mine water quality evaluation.; Established a PCA–PSO–BP model for mine water quality evaluation.; Realized the accurate evaluation and reasonable prediction against the background of big data.; Provide reference for the in-depth research of optimization algorithms and neural networks in the field of water quality evaluation.;http://jwcc.iwaponline.com/content/15/2/593bp neural networkmine water quality evaluationparticle swarm optimization (pso)principal component analysis (pca)pso–bp model |
| spellingShingle | Jiaqi Wang Yanli Huang Evaluation of mine water quality based on the PCA–PSO–BP model Journal of Water and Climate Change bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model |
| title | Evaluation of mine water quality based on the PCA–PSO–BP model |
| title_full | Evaluation of mine water quality based on the PCA–PSO–BP model |
| title_fullStr | Evaluation of mine water quality based on the PCA–PSO–BP model |
| title_full_unstemmed | Evaluation of mine water quality based on the PCA–PSO–BP model |
| title_short | Evaluation of mine water quality based on the PCA–PSO–BP model |
| title_sort | evaluation of mine water quality based on the pca pso bp model |
| topic | bp neural network mine water quality evaluation particle swarm optimization (pso) principal component analysis (pca) pso–bp model |
| url | http://jwcc.iwaponline.com/content/15/2/593 |
| work_keys_str_mv | AT jiaqiwang evaluationofminewaterqualitybasedonthepcapsobpmodel AT yanlihuang evaluationofminewaterqualitybasedonthepcapsobpmodel |