Landslide susceptibility evaluation based on CF integrated with SSA to optimize SVM and RF models
For the traditional modeling process of intra-regional landslide susceptibility evaluation,there may be problems such as non-uniformity of sample data outline and errors in the selection of model parameters.This paper takes Liuba County of Shaanxi Province as the research area,se-lects 10 evaluation factors such as elevation,slope,water system,rainfall,stratigraphic litholo-gy,etc.,and uses the certainty factor model(CF)to calculate the sensitivity of each evaluation factor as a support vector machine model(SVM)and random forest model(RF)input sample at-tribute values;it introduces the sparrow search algorithm(SSA)to optimize the parameters of SVM model and RF model respectively,obtains the optimal parameters to train the two models,and finally constructs CF-SSA-SVM and CF-SSA-RF models,which can predict the entire study area,complete the landslide susceptibility evaluation,and verify the accuracy of the two models through the receiver operating characteristic curve(ROC).The results show that the evaluation results by the two models have more landslide points in the extremely high-prone areas,and no landslide points in the extremely low-prone areas,and that the evaluation results are of high accu-racy.Among them,the AUC values at the success rate and prediction rate curves of the CF-SSA-RF model are 0.994 and 0.940,respectively,which are higher than those by the CF-SSA-SVM model;verified by three typical landslides,the results show that the prone zones and historical landslide points are relatively consistent.It further shows that the CF-SSA-RF model is more suitable for the landslide susceptibility evaluation research in Liuba County,providing a guiding basis for the local landslide disaster risk assessment.
国家科技期刊平台
NSTL
万方数据
