Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM
Bibliographic Data
| ID | 15480905 |
|---|---|
| Authors | Junqi Zhu (0000-0002-5247-9805, Anhui University of Science and Technology), Li Yang (0000-0003-3852-3651, Anhui University of Science and Technology, corresponding author), Xue Wang (0009-0005-0215-9044, Anhui University of Science and Technology, corresponding author), Haotian Zheng (0000-0003-2313-5154, Anhui University of Science and Technology), Mengdi Gu (Anhui University of Science and Technology), Shanshan Li (0000-0001-5116-3588, Anhui University of Science and Technology), Xin Fang (0009-0000-1803-483X, Anhui University of Science and Technology) |
| Year | 2022 |
| Volume | 19 |
| Issue | 19 |
| Pages | 12869-12869 |
| Publication date | 2022-10-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph191912869 |
| PMID | 36232168 |
| OpenAlex | W4303699397 |
| Language | EN |
| Citations received | 2 |
| References cited | 2 |
Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability
Coal · Generalization · Machine learning · Mathematical optimization · Particle swarm optimization · Robustness (evolution · Support vector machine · Swarm behaviour · Coal Properties and Utilization · Computer Science · Engineering · Geoscience and Mining Technology · Mathematics · Safety and Risk Management · Artificial Intelligence
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,5 |
| Citation span | 2022 - 2023 (2) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 2 |