利用随机森林回归模型计算主要影响角正切
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摘要
主要影响角正切(tanβ)是开采沉陷预计的一个重要参数,对于准确界定下沉盆地边界具有重要作用。为快速精确地计算tanβ,进而有效提高开采沉陷预计精度,首先讨论了影响tanβ大小的地质采矿因素,确定了5个基本变量,即开采厚度、煤层倾角、开采深度、工作面斜长、岩性影响系数;然后详细分析了随机森林算法(Random forest,RF)的基本原理及基本实现流程;最后构建了一种计算tanβ的随机森林回归模型,用于训练和测试该回归模型的样本数据来源于国内部分主要矿区建立的典型地表位移观测站的实测资料。对训练后的回归模型采用测试样本进行检验分析,结果表明:1利用该模型计算的tanβ与实测值的最小相对误差为0.381%,最大相对误差为2.563%。2该模型具有较强的泛化能力,在计算tanβ时不仅速度快,而且具有较高的精度,对于高精度计算tanβ有一定的参考价值。
The tangent of major influence angle( tanβ) is an important parameter for mining subsidence prediction,and it plays a key role to determine the subsidence basin boundary. In order to calculate the tanβ value quickly and accurately to improve the accuracy of mining subsidence prediction effectively. Firstly,the geological and mining factors that are affecting the value change of tanβ are discussed in detail,the five basic variables( mining thickness,dip angle of coal seam,mining depth,working face slanting length,lithology influence coefficient) are determined; then,the basic principles and basic implementation process of random forest algorithm( RF) are analyzed in depth; finally,the random forest regression model is established to calculate the tanβ value,the actual measured data of the typical surface movement observation stations that are located in the part of the main mining areas in China are regarded as the training and test samples of the regression model established in this paper. The test samples are used to conduct inspection and analysis of the random forest regression model after training,the results show that: 1the minimum relative error and maximum relation error between the tanβ calculated by the regression model established in this paper and the actual measured data are 0. 381% and 2. 563% respectively. 2the regression model established in this paper has stronger generalization ability,it has the characteristics of fast calculation speed and high accuracy in the calculation process of tanβ,besides that,it also has a good application prospect in the practical engineering,it can provide reference for calculating the tanβ value with high accuracy.
The tangent of major influence angle( tanβ) is an important parameter for mining subsidence prediction,and it plays a key role to determine the subsidence basin boundary. In order to calculate the tanβ value quickly and accurately to improve the accuracy of mining subsidence prediction effectively. Firstly,the geological and mining factors that are affecting the value change of tanβ are discussed in detail,the five basic variables( mining thickness,dip angle of coal seam,mining depth,working face slanting length,lithology influence coefficient) are determined; then,the basic principles and basic implementation process of random forest algorithm( RF) are analyzed in depth; finally,the random forest regression model is established to calculate the tanβ value,the actual measured data of the typical surface movement observation stations that are located in the part of the main mining areas in China are regarded as the training and test samples of the regression model established in this paper. The test samples are used to conduct inspection and analysis of the random forest regression model after training,the results show that: 1the minimum relative error and maximum relation error between the tanβ calculated by the regression model established in this paper and the actual measured data are 0. 381% and 2. 563% respectively. 2the regression model established in this paper has stronger generalization ability,it has the characteristics of fast calculation speed and high accuracy in the calculation process of tanβ,besides that,it also has a good application prospect in the practical engineering,it can provide reference for calculating the tanβ value with high accuracy.
引文
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[2]邹友峰,邓喀中,马伟民.矿山开采沉陷工程[M].徐州:中国矿业大学出版社,2003.Zou Youfeng,Deng Kangzhong,Ma Weimin.Mining Subsidence Engineering[M].Xuzhou:China University of Mining and Technology Press,2003.
[3]李培现,谭志祥,邓喀中.地表移动概率积分法计算参数的相关因素分析[J].煤矿开采,2011,16(6):14-18.Li Peixian,Tan Zhixiang,Deng Kazhong.Related factors analysis of calculation parameters of probability integral method for surface movement[J].Coal Mining Technology,2011,16(6):14-18.
[4]吴朝阳,李宁.AGO-BP神经网络在主要影响角正切求取中的应用[J].煤矿安全,2012,43(3):117-120.Wu Chaoyang,Li Ning.Application of AGO-BP neural network to the tangent calculation method of the main influencing angle[J].Safety in Coal Mines,2012,43(3):117-120.
[5]高彦涛,谭志祥,邓喀中.基于神经网络的主要影响角正切值求取方法[J].煤矿安全,2007,38(4):30-33.Gao Yantao,Tan Zhixiang,Deng Kazhong.Calculating tangent method of major influence angle based on neural network[J].Safety in Coal Mines,2007,38(4):30-33.
[6]魏好,邓喀中,卢正,等.基于支持向量机的主要影响角正切求取方法研究[J].金属矿山,2010(5):120-122.Wei Hao,Deng Kazhong,Lu Zheng.et al.Study on the tangent calculation method of the main influencing angle based on SVM[J].Metal Mine,2010(5):120-122.
[7]陈俊杰,王明远,武君,等.基于PSO-RBF神经网络的主要影响角正切求取方法[J].金属矿山,2015(4):224-228.Chen Junjie,Wang Mingyuan,Wu Jun,et al.Calculating tangent method of major influence angle based on PSO-RBF neural network[J].Metal Mine,2015(4):224-228.
[8]崔东文.随机森林回归模型及其在污水排放量预测中的应用[J].供水技术,2014(1):31-36.Cui Dongwen.Application of random forest regression model for waste water discharge forecasting[J].Water Technology,2014(1):31-36.
[9]郭文兵,谭志祥,陈俊杰,等.煤矿开采损害与保护[M].北京:煤炭工业出版社,2013.Guo Wenbing,Tan Zhixiang,Chen Junjie,et al.Mining Damage and Protection[M].Beijing:China Coal Industry Publishing House,2013.
[10]崔东文,金波.基于随机森林回归算法的水生态文明的综合评价[J].水利水电科技进展,2014,34(5):56-60.Cui Dongwen,Jin Bo.Comprehensive evaluation of water ecological civilization based on random forests regression algorithm[J].Advances in Science and Technology of Water Resources,2014,34(5):56-60.
[11]王丽爱,马昌,周旭东,等.基于随机森林回归算法的小麦叶片SPAD值遥感估算[J].农业机械学报,2015,46(1):259-265.Wang Liai,Ma Chang,Zhou Xudong,et al.Estimation of wheat leaf SPAD value using RF algorithmic model and remote sensing data[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(1):259-265.
[12]国家煤炭工业局.建筑物、水体、铁路及主要井巷煤柱留设与压煤开采规程[M].北京:煤炭工业出版社,2000.China Coal Industry Bureau.The Building,Water body,Railroad and the Main Well Lane Coal Column Remains Supposes with Presses the Coal Mining Regulation[M].Beijing:China Coal Industry Publishing House,2000.