基于鲸鱼算法优化支持向量机的露天煤矿边坡稳定性预测

Slope stability prediction based on whale algorithm optimizing support vector machine for open-pit coal slopes

边坡的稳定性对露天矿山的安全影响重大,为了快速地对露天煤矿边坡稳定状态进行判断,提出了一种基于鲸鱼算法(WOA)优化支持向量机(SVM)的露天煤矿边坡稳定性预测模型。该方法使用WOA对SVM模型的惩罚系数及核函数参数的取值进行了优化,解决了SVM模型的初始参数值选取困难的缺点,利用WOA优化后的SVM模型对收集到的边坡数据进行预测,并与RF、BP、SVM模型的预测结果进行对比。结果表明,WOA优化后的SVM模型具有更高的预测精度,该模型对确定露天煤矿边坡稳定状态有一定的参考价值。

The stability of the slope has a significant impact on the safety of open-pit coal mine.In order to quickly determine the stability status of slopes in open-pit coal mine slopes,an open-pit coal mine slope stability prediction model based on the whale algorithm(WOA)optimized support vector machine(SVM)is proposed.This method uses WOA to optimize the penalty coefficient and kernel function parameter of WOA model,which solves the problems of difficult selection of SVM model parameters.The WOA-optimized SVM model was used to predict the collected slope data and compared with the prediction results of RF,BP and SVM models.The results show that the WOA-optimized SVM model has higher prediction accuracy,which has a certain reference value for determining the stability state of open-pit coal mine slopes.