摘要
对煤矿关键测点的瓦斯浓度进行科学且准确的预测,是防范瓦斯灾害的关键.为了从瓦斯监测监控系统实时采集的煤矿安全环境数据中分析和挖掘瓦斯浓度信息,从而进行关键测点的瓦斯浓度预测,本文采用等度量映射算法(Isomap)结合支持向量回归算法(SVR)来预测瓦斯浓度.该方法首先通过Isomap算法将非线性的高维煤矿井下安全环境数据进行维数约减,然后利用SVR算法进行回归预测.通过实验分析与对比,该方法行之有效,与多元线性回归(MLR)、支持向量回归(SVR)方法相比,在预测精度上有一定的优势,且在瓦斯波动异常情况下,鲁棒性更强.
The key to prevent gas disasters is to scientifically and accurately predict the gas concentration of key measuring points in coal mines. In order to analyze and mining the gas concentration information from the real-time data of coal mine safety environment collected by the gas monitoring system, and then predict the gas concentration of key measuring points, this paper proposes a method based on Isomap and SVR to predict the gas concentration. This method first reduces the non-linear high-dimensional mine safety environment data by Isomap, and then uses SVR algorithm for regression prediction. Through experimental analysis and comparison, this method has some advantages over MLR and SVR in prediction accuracy, and has stronger robustness in case of abnormal gas fluctuation.
作者
吴海波
施式亮
念其锋
Wu Haibo;Shi Shiliang;Nian Qifeng(School of Resources and Safety Engineering, Central South University, Changsha 410082, China;Hunan Provincial Key Laboratory of Safe Mining Techniques of Coal Mines, Xiangtan 411201, China;School of Resources, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, China)
出处
《矿业工程研究》
2019年第2期51-54,共4页
Mineral Engineering Research
基金
国家自然科学基金资助项目(51774135)
煤矿安全开采技术湖南省重点实验室开放基金资助项目(201304)
湖南科技大学煤炭资源清洁利用与矿山环境保护湖南省重点实验室开放基金资助项目(E21701)
湖南省高等学校科学研究优秀青年资助项目(14B058)
湖南省普通高等学校教学改革研究资助项目(2017-237)
作者简介
通信作者:吴海波,E-mail:hbwu73@sina.com.
