| 支持向量机在矿井突水水源识别中的应用 |
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| 引用本文： | 陈祖云,张桂珍,邬长福,杨胜强.支持向量机在矿井突水水源识别中的应用[J].南方冶金学院学报,2009,30(5):10-13. |
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| 作者姓名： | 陈祖云 张桂珍 邬长福 杨胜强 |
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| 作者单位： | 陈祖云,张桂珍,邬长福(江西理工大学资源与环境工程学院,江西,赣州,341000);杨胜强(中国矿业大学安全工程学院,江苏,徐州,221008) |
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| 基金项目： | 江西省教育厅资助项目，江西省安全生产监督管理局资助项目 |
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| 摘 要： | 为了正确地识别矿井突水水源，基于水化学成分对水源判别的重要性，选择K^＋ ＋Na^＋、Ca2^＋、Mg2^＋、Cl^-、SO4^2-、HCO3^-这6项指标作为特征向量，建立了矿井突水水源的支持向量机识别模型．此方法不仅结构简单，而且技术性能尤其泛化能力与BP神经网络相比有明显提高，能有效地识别矿井突水水源的类别，为防治水工作提供决策依据．
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| 关 键 词： | 矿井突水 水源识别 支持向量机 水化学成分 |
Application of Water Source Identification in Mine Inflow Based on Support Vector Machines |
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| Affiliation: | CHEN Zu-yun, ZHANG Gui-zhen, WU Chang-fu, YANG Sheng-qiang ( 1. Faculty of Resource and Environmental Engineering, Jiangxi University of Science and Technology,Ganzhou 341000,China; 2. Faculty of Safety Science Engineering, China University of Mining and Technology, Xuzhou, 221008,China ) |
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| Abstract: | In order to identify the water source of mine inflow correctly, the K^＋ ＋Na^＋ Ca2^＋,Mg2^＋ Cl^-, SO4^2- ,HCO3^- are selected as the feature vectors based on the importance of 6 water hydrochemical element factors. Then, the determination model of water source in mine inflow is established based on Support Vector Machines in the paper. The results show that the SVM method is not only simple in structure, but also has markedly improved in technical performance and generalization ability, especially compared with the BP neural network. The method is able to identify the water source of mine inflow effectively based on support vector machines model, and provids basis for making decision on prevention and cure of mine inflow work. |
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| Keywords: | mine inflow water identification support vector machine water chemical component |
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