Research on Slope Stability Prediction Method Based on Intelligent Optimization Algorithm
Aiming at the issues of biased data analysis and poor model accuracy in prediction of slope stability,six variable features are chosen based on 302 slope examples,and the BP neural network's sensitive parameters are updated by the sparrow search algorithm(SSA).The SSA-BP slope stability prediction model is established.As measurement indi-cators,the confusion matrix,the ROC curve,and the area under the curve(AUC)value were employed.The five-fold cross-validation procedure increased the model's capacity for generalization.The five machine learning algorithms of RF,BP,SVM,PSO-BP,GA-BP and LSTM were compared.The results show that the AUC value,accuracy and F1 score of the SSA-BP model were the highest,and the responding values reached 91.90%,85.81%and 85.87%,respectively,which was 23%higher than that of the BP network before optimization.The classical example proved that the SSA-BP prediction model is similar to the safety factor calculated by ABAQUS,which can give reliable prediction results.Thus,it provides a new way for slope stability prediction in geotechnical engineering.
NETL
NSTL
万方数据
