THE SLOPE STABILITY ANALYSIS BASED ON MACHINE LEARNING
441 slope cases in northern Zhejiang province are taken to analyze the stable state of the slope more quickly and accurately by establishing the data set in terms of the relative density,cohesion,internal friction angle,slope angle,slope height,and pore pressure ratio.By comparing the prediction accuracy of support vector machines,BP neural networks,random forests,GA-BP,and PSO-BP models,which have shown good performance in predict-ing the stability of slopes,the algorithm model with the highest accuracy under this sample is obtained.Research shows that,based on the confusion matrix and AUC metrics for model accuracy evaluation,PSO-BP has the advan-tages of small error and strong stability,providing a new approach for faster analysis of slope stability in the future.