Prediction of slope safety factor based on IPOA-SVR model
The safety factor is one of the important indicators used to evaluate slope stability.The complex slope system leads to the uncertainty of safety factor prediction.Therefore,in order to obtain a more reliable safety factor and solve the shortcoming that the Pelican Algorithm(POA)would easily fall into a local optimum as the number of iterations increases,the regression model combined with multi-strategy Pelican Algorithm(IPOA)and support vectors machine(SVR)was proposed to predict the slope safety factor.First,the original Pelican algorithm was improved by integrating multiple strategies.Then the improved Pelican algorithm was combined with the support vector machine to select six influencing factors as input layer indicators of the IPOA-SVR model and train the model to obtain IPOA-SVR slope stability prediction model.Finally,compared with KNN,RF and Adaboost models,the mean square errors(MSE)of each model on the training set and test set were calculated to verify the superiority of the IPOA-SVR model.The experimental results showed that compared with other models,the IPOA-SVR model had better optimization performance.The mean square error on the test set was 0.030 9,and the correlation coefficient was 0.91.This illustrates the effectiveness of the strategy used in this article for the POA algorithm.And the IPOA-SVR model can provide a solid technical foundation for the prediction of slope instability disasters.
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