| 基于PCA-AHPSO-SVR的煤层瓦斯含量预测研究 |
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| 引用本文： | 魏国营,裴蒙.基于PCA-AHPSO-SVR的煤层瓦斯含量预测研究[J].中国安全生产科学技术,2019,15(3):69-74. |
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| 作者姓名： | 魏国营 裴蒙 |
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| 作者单位： | （1.河南理工大学 安全科学与工程学院，河南 焦作 454000; 2.河南省瓦斯地质与瓦斯治理重点实验室—省部共建国家重点实验室培育基地，河南 焦作 454003） |
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| 基金项目： | 收稿日期: 2018-12-18 |
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| 摘 要： | 为了提高煤层瓦斯含量预测的准确性和科学性，通过主成分分析方法对影响煤层瓦斯含量的7个因素进行特征提取，消除影响因素之间的相关性，减少维度；用支持向量回归机对提取的因素进行训练，并用改进的自适应混合粒子群算法对SVR的参数进行优化，提出PCA-AHPSO-SVR模型；与PCA-PSO-SVR,PSO-SVR这2个模型在相同环境下进行30次运行比较。研究结果表明:研究提出的PCA-AHPSO-SVR模型较其他2种模型平均准确率分别提高5.51%和9.32%，稳定性更佳，可满足工程实际需求。
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| 关 键 词： | 煤层瓦斯含量 主成分分析 自适应混合粒子群算法 支持向量回归机 预测 |
Prediction of coal seam gas content based on PCA AHPSO SVR |
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| WEI Guoying,' target="_blank" rel="external">,PEI Meng.Prediction of coal seam gas content based on PCA AHPSO SVR[J].Journal of Safety Science and Technology,2019,15(3):69-74. |
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| Authors: | WEI Guoying " target="_blank">' target="_blank" rel="external"> PEI Meng |
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| Affiliation: | (1.College of Safety Science and Engineering, Henan Polytechnic University, Jiaozuo Henan 454000,China;2.State Key Laboratory Cultivation Base for Gas Geology and Gas Control (Henan Polytechnic University), Jiaozuo Henan 454003，China) |
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| Abstract: | In order to improve the accuracy and scientificity of coal seam gas content prediction, Principal Component Analysis (PCA) is used to extract the characteristics of seven factors affecting coal seam gas content, eliminating the correlation between influencing factors and reducing the dimension，Then the support factors are trained by Support Vector Regression (SVR), and the parameters of SVR are optimized by the improved Adaptive Hybrid Particle Swarm Optimization (AHPSO)，the PCA AHPSO SVR model was proposed and compared with the PCA PSO SVR and PSO SVR models in the same environment for 30 times，the results show that the average accuracy of the model is increased by 5.51% and 9.32% respectively，the stability is better, and the actual needs of the project are met. |
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| Keywords: | coal seam gas content principal component analysis adaptive particle swarm optimization (APSO) support vector regression machine（SVR） prediction |
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