Study on Water-Richness of Roof Sandstone Based on Hybrid Kernel Function Support Vector Machine
ZHANG Liangliang
SHI Yongkui
LI Junyong
Abstract：In order to find a better method to predict the level of sandstone water enrichment of coal roof,taking Sangshuping Coal Mine as an example,the BP neural network,K-nearest neighbor classification,decision tree and support vector machine algorithm were used to establish the level of sandstone water enrichment of coal roof.By comparison,the accuracy of prediction model based on SVM was 87.5%,the node error rate was the lowest,better than the other three models.In order to further improve the prediction accuracy,a predictive model of mixed kernel function support vector machine was established,and the prediction accuracy was 100% when λ1= 0.05 and λ2= 0.95.The results showed that:the mixed kernel function with conditional attribute as input and decision attribute as output can predict the grade of sandstone water enrichment in coal seam roof,and the effect is good.
Keywords：sandstone water enrichmentBP neural networknaive bayes classifiersupport vector machinemixed kernel function
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 72-76 )