基于GAPSO-RFR的矿井底板突水预测模型与应用
投稿时间:2019-05-28  修订日期:2020-08-05  点此下载全文
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作者单位E-mail
师煜 北京信息科技大学 信息与通信工程学院 shiyucivi@163.com 
朱希安 北京信息科技大学 信息与通信工程学院 Zhuxian1962@163.com 
王占刚 北京信息科技大学 信息与通信工程学院  
刘德民 华北科技学院 安全工程学院  
基金项目:国家重点研发计划项目“水灾应急决策支持专家系统”资助(编号:2017YFC0804108);北京市教委科研计划项目(KM201811232010);北京市科技创新服务能力建设-基本科研业务费(市级)(科研类)(PXM2018_014224_000032)
中文摘要:为了更精准的预测矿井突水灾害,对突水的预测和救援提供帮助,减小水灾造成的损失,提出了基于GAPSO-RFR的矿井突水预测模型。利用遗传-粒子群算法对随机森林回归模型(RFR)进行优化。选取34例样本对GAPSO-RFR模型进行迭代和训练。测试结果表明,GAPSO-RFR模型提高了预测精度,减少了泛化误差。同时利用模型对王家岭矿区部分盘区的10号与2号煤层的突水风险进行预测分析,得出了突水风险较高的区域分布情况。
中文关键词:矿井水灾  突水预测  粒子群优化  遗传算法  随机森林
 
Forecast model of mine floor water inrush based on genetic particle swarm optimization and random forest regression and its application
Abstract:In order to predict mine water inrush disaster more accurately, provide help for water inrush prediction and rescue, and reduce the loss caused by flood, a mine water inrush prediction model based on GAPSO-RFR is proposed. Genetic particle swarm optimization algorithm(GAPSO) was used to optimize the random forest regression(RFR) model. 34 samples were selected to iterate and train the GAPSO-RFR model, and the optimal parameters were obtained. The test results showed the GAPSO-RF model improved the prediction accuracy and reduced the generalization error. Model was used to predict the risk of water inrush in some mining areas of Wangjialing coalfield. The regional distribution of high risk of water inrush was gained.
keywords:mine water disaster  water inrush prediction  particle swarm optimization  genetic algorithm  random forest
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