基于FOA-SVR模型的矿井底板突水量预测应用研究
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摘要
矿井突水是常见的突发性强烈的矿井灾害。为了更好地预防矿井水灾,降低灾害造成的物质损失,减少人员伤亡,建立了一种基于FOA-SVR的矿井底板突水量预测模型,利用果蝇算法优化支持向量回归机算法(FOA-SVR)选出最优的模型参数。针对底板突水这种非线性、小样本问题,从突水因素中选取水压、含水层厚度、隔水层厚度、底板采动裂隙带深度以及断层落差作为特征因素。然后利用FOA对SVR参数进行优化之后建立FOA-SVR底板突水量预测模型,输出即为需要预测的突水量。结合实例并将该模型的预测结果与SVR模型的预测结果进行对比,结果表明:该模型在预测突水量的精度上比SVR模型更高,具有一定的应用价值。
Mine water inrush is a common mine disaster which is sudden and intense.In order to better prevent mine floods and reduce material losses and casualties caused by disasters,aprediction model for mine floor water inrush quantity is built by studying the principle and characteristics of FOA-SVR model,using the fruit fly algorithm(FOA)to optimize support vector regression algorithm(SVR)to select the excellent model parameters.For the nonlinear,small sample problem of floor water inrush,water pressure,aquifer thickness,aquiclude thickness,height of water flowing fractured and fault throw are selected as the characteristic factors.After the SVR parameters are optimized by the fruit fly algorithm,the FOA-SVR prediction model for floor water inrush quantity is built,and the output is the water inrush quantity that we need to predict.Comparing the predicted results of FOA-SVR model with the predicted results of SVR model,the results show that,the model is more accurate than the SVR model in the accuracy of predicting water inrush quantity and has certain application value.
Mine water inrush is a common mine disaster which is sudden and intense.In order to better prevent mine floods and reduce material losses and casualties caused by disasters,aprediction model for mine floor water inrush quantity is built by studying the principle and characteristics of FOA-SVR model,using the fruit fly algorithm(FOA)to optimize support vector regression algorithm(SVR)to select the excellent model parameters.For the nonlinear,small sample problem of floor water inrush,water pressure,aquifer thickness,aquiclude thickness,height of water flowing fractured and fault throw are selected as the characteristic factors.After the SVR parameters are optimized by the fruit fly algorithm,the FOA-SVR prediction model for floor water inrush quantity is built,and the output is the water inrush quantity that we need to predict.Comparing the predicted results of FOA-SVR model with the predicted results of SVR model,the results show that,the model is more accurate than the SVR model in the accuracy of predicting water inrush quantity and has certain application value.
引文
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[12]孙秋韵,刘金清,刘引,等.基于改进GA参数优化的SVR股价预测模型[J].计算机系统应用,2015,24(9):29-34.SUN Qiuyun,LIU Jinqing,LIU Yin,et al.Stock price prediction model based on SVR with parameters optimized by improved GA[J].Computer Systems&Applications,2015,24(9):29-34.
[2]曹庆奎,赵斐.基于遗传-支持向量回归的煤层底板突水量预测研究[J].煤炭学报,2011,36(12):2097-2101.CAO Qingkui,ZHAO Fei.Forecast of water inrush quantity from coal floor based on genetic algorithm-support vector regression[J].Journal of China Coal Society,2011,36(12):2097-2101.
[3]刘北战,梁冰.基于PCA-SVR的煤层底板突水量预测[J].煤田地质与勘探,2011,39(1):28-30.LIU Beizhan,LIANG Bing.Prediction of seamfloor water inrush based on combining principal component analysis and support vector regression[J].Coal Geology&Exploration,2011,39(1):28-30.
[4]高卫东,王正帅.基于粒子群优化支持向量机的煤层底板突水量等级预测[J].煤田地质与勘探,2012,40(6):44-47.GAO Weidong,WANG Zhengshuai.Forecast of inrushed water volume grade from coal floor based on support vector machine with particle swarm optimization[J].Coal Geology&Exploration,2012,40(6):44-47.
[5]高雷阜,高晶,赵世杰.人工蜂群算法优化SVR的预测模型[J].计算机工程与应用,2016,52(11):55-59.GAO Leifu,GAO Jing,ZHAO Shijie.Forecast model of SVR optimized by artificial bee colony algorithm[J].Computer Engineering and Applications,2016,52(11):55-59.
[6]司刚全,李水旺,石建全,等.采用改进果蝇优化算法的最小二乘支持向量机参数优化方法[J].西安交通大学学报,2017,51(6):14-19.SI Gangquan,LI Shuiwang,SHI Jianquan,et al.Least squares support vector machine parameters optimization based on improved fruit fly optimization algorithm with applications[J].Journal of Xi'an Jiaotong University,2017,51(6):14-19.
[7]吴小文,李擎.果蝇算法和5种群智能算法的寻优性能研究[J].火力与指挥控制,2013,38(4):17-20.WU Xiaowen,LI Qing.Research of optimizing performance of fruit fly optimization algorithm and five kinds of intelligent algorithm[J].Fire Control&Command Control,2013,38(4):17-20.
[8]刘亮,姚剑飞,高树成,等.基于果蝇算法的转子不平衡参数辨识研究[J].北京化工大学学报:自然科学版,2018,45(3):79-83.LIU Liang,YAO Jianfei,GAO Shucheng,et al.Identification of rotor unbalance parameters based on a fruit fly algorithm[J].Journal of Beijing University of Chemical Technology:Natural Science,2018,45(3):79-83.
[9]高昆仑,刘建明,徐茹枝,等.基于支持向量机和粒子群算法的信息网络安全态势复合预测模型[J].电网技术,2011,35(4):176-182.GAO Kunlun,LIU Jianming,XU Ruzhi,et al.A hybrid security situation prediction model for information network based on support vector machine and particle swarm optimization[J].Power System Technology,2011,35(4):176-182.
[10]周科,王炳强.隔水层等效厚度的评价确定[J].煤炭与化工,2016,39(2):45-47.ZHOU Ke,WANG Bingqiang.The evaluation of equivalent thickness of water-resisting layer[J].Coal and Chemical Industry,2016,39(2):45-47.
[11]黄存捍,冯涛,王卫军,等.断层影响下底板隔水层的破坏机理研究[J].采矿与安全工程学报,2010,27(2):219-222.HUANG Cunhan,FENG Tao,WANG Weijun,et al.Research on the failure mechanism of water-resisting floor affected by fault[J].Journal of Mining&Safety Engineering,2010,27(2):219-222.
[12]孙秋韵,刘金清,刘引,等.基于改进GA参数优化的SVR股价预测模型[J].计算机系统应用,2015,24(9):29-34.SUN Qiuyun,LIU Jinqing,LIU Yin,et al.Stock price prediction model based on SVR with parameters optimized by improved GA[J].Computer Systems&Applications,2015,24(9):29-34.
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