宁夏工程技术2023，Vol.22Issue(4)：359-365,7.
基于SSA-SVM的煤矿瓦斯监测预警模型研究
Research on Coal Mine Gas Monitoring and Early Warning Model Based on SSA-SVM
摘要
Abstract
Confronted with the substantial data volumes characteristic of coal mining operations and the complex array of environmental factors impinging upon methane pre-warning systems,a sophisticated gas monitoring and preemptive alerting model predicated on the Sparrow Search Algorithm(SSA)integrated with Support Vector Machine(SVM)has been advanced.Initially,Internet of Things frameworks facilitate the systematic aggregation and surveillance of production-related environmental datasets.Subsequently,the SSA is deployed to refine the penalty coefficient and kernel parameters intrinsic to the SVM,culminating in the formulation of a robust SSA-SVM safety prognosticative model.This model undergoes rigorous training and is instrumental in the categorization and early warning of methane hazard levels.Empirical analyses corroborate the model's prognostic efficacy,evidencing a predictive accuracy of 91.667%,thus significantly bolstering the methane safety early warning mechanisms within coal mining infrastructures.关键词
煤矿瓦斯/预警模型/麻雀搜索算法Key words
coal mine gas/early warning model/sparrow search algorithm分类
信息技术与安全科学引用本文复制引用
张朝,冯锋..基于SSA-SVM的煤矿瓦斯监测预警模型研究[J].宁夏工程技术,2023,22(4):359-365,7.基金项目
宁夏重点研发计划项目(2022BEG02016) （2022BEG02016）
宁夏自然科学基金重点项目(2021AAC02004) （2021AAC02004）
