摘要
为提高煤层瓦斯含量预测的准确性和效率,提出了一种基于灰色关联度分析(GRA)、蜣螂优化(DBO)算法和支持向量回归(SVR)模型的瓦斯含量预测方法.采用GRA筛选影响瓦斯含量的因素来降低预测模型输入数据的维度,通过DBO算法对SVR模型的参数进行优化,构建基于GRA-DBO-SVR的瓦斯含量预测模型,并对GRA-DBO-SVR、GRA-PSO-SVR、GRA-SVR和SVR模型的预测结果进行对比.结果表明:GRA-DBO-SVR、GRA-PSO-SVR、GRA-SVR和SVR模型的MRE分别为 2.82%、2.98%、3.72%和 6.02%,MAE分别为 0.28、0.31、0.44 和 0.63,MSE分别为 0.17、0.18、0.37 和 0.90,GRA-DBO-SVR模型具有更好的泛化能力,满足工程实际需要.
Abstract
To improve the accuracy and efficiency of coal seam methane content prediction,a novel gas content prediction method based on Grey Relational Analysis(GRA),Dung Beetle Optimization(DBO)algorithm,and Support Vector Regression(SVR)model was proposed.First,the GRA is used to screen factors that affect gas content to reduce the dimensionality of the input data for the prediction model.Then,the DBO is employed to optimize the parameters of SVR model,constructing a gas content prediction model based on GRA-DBO-SVR.The prediction results of GRA-DBO-SVR,GRA-PSO-SVR,GRA-SVR,and SVR models are compared.The results show that the Mean Relative Errors(MRE)of GRA-DBO-SVR,GRA-PSO-SVR,GRA-SVR,and SVR are 2.82%,2.98%,3.72%,and 6.02%,respectively;the Mean Absolute Errors(MAE)are 0.28,0.31,0.44,and 0.63,respectively;and the Mean Squared Errors(MSE)are 0.17,0.18,0.37,and 0.90,respectively.The GRA-DBO-SVR model demonstrates better generalization ability,meeting the actual needs of engineering applications.
维普
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