Article
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A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment
Version 1
: Received: 21 June 2023 / Approved: 21 June 2023 / Online: 21 June 2023 (13:57:52 CEST)
A peer-reviewed article of this Preprint also exists.
Li, Z.; Feng, F. A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment. Sensors 2023, 23, 6614. Li, Z.; Feng, F. A Safety Warning Model Based on IAHA-SVM for Coal Mine Environment. Sensors 2023, 23, 6614.
Abstract
Coal is an important resource that is closely related to people’s lives and has an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. To this end, 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, the 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 then the simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model was established using the improved algorithm to classify and predict the safety of the coal mine environment into 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 the performance of the IAHA-SVM model has a significant improvement.
Keywords
coal mine safety; artificial hummingbird algorithm; tent chaotic mapping; levy flight; simplex method; support vector machine
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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