基于PCA-GWO-SVM的矿山边坡变形预测

Deformation prediction of mine slope based on PCA-GWO-SVM

针对矿山边坡预测模型精度低等问题,提出一种由主成分分析(PCA)、灰狼算法(GWO)和支持向量机(SVM)组合的混合模型(PCA-GWO-SVM)。首先,采用PCA对原始数据进行降维去噪;其次,通过GWO算法优化支持向量机参数;最后,通过SVM实现矿山边坡变形的预测。矿山边坡实例表明PCA-GWO-SVM模型具有更高的预测精度。

Aiming at the low precision of mine slope prediction model,a hybrid model(PCA-GWO-SVM)consisting of principal component analysis(PCA),grey wolf algorithm(GWO)and support vector machine(SVM)was proposed.Firstly,PCA was used to reduce the noise of the original data.Secondly,the support vector machine parameters were optimized by GWO algorithm.Finally,the deformation prediction of mine slope was realized by SVM.The mine slope example showed that the PCA-GWO-SVM model had higher prediction accuracy.