A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment
Coal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect ab...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/14/6614 |
| _version_ | 1827731732084817920 |
|---|---|
| author | Zhen Li Feng Feng |
| author_facet | Zhen Li Feng Feng |
| author_sort | Zhen Li |
| collection | DOAJ |
| description | Coal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved. |
| first_indexed | 2024-03-11T00:40:01Z |
| format | Article |
| id | doaj.art-5e85755e361d40be9e9dd03431effdfa |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:40:01Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5e85755e361d40be9e9dd03431effdfa2023-11-18T21:20:11ZengMDPI AGSensors1424-82202023-07-012314661410.3390/s23146614A Safety Warning Model Based on IAHA-SVM for Coal Mine EnvironmentZhen Li0Feng Feng1School of Information Engineering, Ningxia University, Yinchuan 750021, ChinaSchool of Information Engineering, Ningxia University, Yinchuan 750021, ChinaCoal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.https://www.mdpi.com/1424-8220/23/14/6614coal mine safetyartificial hummingbird algorithmTent chaotic mappingLevy flightsimplex methodsupport vector machine |
| spellingShingle | Zhen Li Feng Feng A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment Sensors coal mine safety artificial hummingbird algorithm Tent chaotic mapping Levy flight simplex method support vector machine |
| title | A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment |
| title_full | A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment |
| title_fullStr | A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment |
| title_full_unstemmed | A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment |
| title_short | A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment |
| title_sort | safety warning model based on iaha svm for coal mine environment |
| topic | coal mine safety artificial hummingbird algorithm Tent chaotic mapping Levy flight simplex method support vector machine |
| url | https://www.mdpi.com/1424-8220/23/14/6614 |
| work_keys_str_mv | AT zhenli asafetywarningmodelbasedoniahasvmforcoalmineenvironment AT fengfeng asafetywarningmodelbasedoniahasvmforcoalmineenvironment AT zhenli safetywarningmodelbasedoniahasvmforcoalmineenvironment AT fengfeng safetywarningmodelbasedoniahasvmforcoalmineenvironment |