Prediction Algorithm of Pre-slip Stage Based on Double Sampling Random Forest:A Case Study of No.5 Block in Huangshi,Hubei Province
In the monitoring process of gravel soil slope,there is a serious mismatch between the monitoring data of the landslide stable deformation stage and the pre-sliding stage,which leads to the small amount of data in the pre-sliding stage,and the unbalanced data set resulting in the inaccurate prediction.A prediction algorithm of surface displacement in the pre-slip stage of gravel soil slope based on DST random forest was proposed.Firstly,the threshold comparison method of surface displacement pre-slip was used to deter-mine whether there is an unbalanced data set.If there is an unbalanced data set,the double sampling technology(DST)was used to collect the unbalanced data set,and the negative samples were oversampled to improve the proportion of negative sample data set.Un-dersampling of positive sample data is carried out to reduce the proportion of positive sample data set.Equal amount of random positive and negative samples were selected as the training set to be processed,and the random forest algorithm was built to test the processed training set,and the test set after training was compared with the predicted results before the collection of non-equilibrium data set.Fi-nally,by comparing the error values and error rate before and after sampling,it is verified that the prediction error rate of DST random forest prediction algorithm is reduced to 3.39％compared with the ordinary random forest prediction algorithm(the prediction error rate is 4.66％),which proves the necessity of double sampling technology(DST)to collect non-equilibrium data set in the pre-slip stage.Finally,it is concluded that DST random forest algorithm can obviously improve the pre-warning effect of the pre-slip stage,and over-come the problems of voting average,stagnation and increasing error rate.
soil landslidepre-slip stagesurface displacementrandom forestnon-balanced data setdouble sampling technique
万方数据
维普
