基于支持向量机的边坡稳定性预测

为准确快速地对边坡进行稳定性分析,提出基于支持向量机(Support vector machine SVM)的稀土矿开采边坡的SVM预测模型,结合42个稀土矿开采边坡实际案例,并分别采用网格寻优算法、遗传算法(Genetic algorithm GA)、粒子群算法(Particle swarm optimization...

The Prediction of the Slope Stability of In-situ Leaching of Ionic Rare-earth Ore Based on SVM

To predict the rare earth ions in-situ leaching mine slope stability accurately,the prediction model of support vector machine(SVM)slope of the rare earth ions in-situ leaching mine slope stability was proposed,combining with 42-ionic rare earth mine slope measured data establish SVM prediction models,Three parameter optimization algorithms based on grid search,genetic algorithm(GA)and particle swarm optimization(PSO)were used respectively.The parameters obtained by three algorithms are analyzed and compared,and the algorithm which most fits the SVM model of prediction of the rare earth ions in-situ leaching mine slope stability was confirmed.The results show that PSO algorithm is suitable for ionic rare earth-situ leaching mine slope stability prediction,the prediction accuracy of the algorithm is highest.