Evaluation of landslide susceptibility based on LR-ANN-SVM

In the susceptibility analysis for landslide, methods like traditional Big Data machine learning are over-emphasis on evaluation of the accuracy of the model. Landslides risk warning will be given toreduce damages in medium-susceptibility and low-susceptibility areas. Three common learning methods-artificial neural network (ANN), logistic regression (LR), support vector machines (SVM) -- were selected in this research to evaluate landslide susceptibility in Shangyou County. Shangyou County was divided into high, higher, medium, lower, and low susceptibility areas. Shown by the values of the area under the curve (AUC): AUC of artificial neural network (ANN)=0.939, AUC of logistic regression (LR)=0.897, AUC of support vector machine (SVM)=0.884. The data have high evaluation precision. According to the above evaluation, the latent semantic index (LSI) of the raster in Shangyou County is obtained. Based on the MAX (LSI (LR), LSI(ANN), LSI(SVM)) function, maximum value of thesusceptibility of the above model was obtainedto evaluate the susceptibility of Shangyou County. The results show that the AUC of LR-ANN-SVM=0.815, which has a relevantly high accuracy of susceptibility evaluation. According to the proportion of landslides in the high- susceptibility areas and the higher- susceptibility areas, the proportions of landslides in LR, ANN, SVM, and LR-ANN-SVM are 80.6%, 74.6%, 91%, and 93.2% respectively, indicating that ANN-LR-SVM susceptibility partition governance is more secure.