A comparison of five methods in landslide susceptibility assessment: a case study from the 330-kV transmission line in Gansu Region, China

Landslides cause damages to land and infrastructure and pose serious threat to human survival. To prepare a landslide susceptibility map of the region in Longnan, City Gansu Province, with a 330-kV transmission line, 10 parameters were selected by correlation analysis and sensitivity analysis from initial 18 and five different methods were used, including analytical hierarchy process (AHP), information value (IV), fractal theory (FT), back propagation neural network (BPNN), support vector machine (SVM). The susceptibility maps were validated through receiver operating characteristic (ROC) and cumulative landslides percentage curves based on 77 existing landslide events. The results indicate that BPNN and SVM model are most accurate, time-saving and easily implemented. All of the five methods accurately predict the spatial distribution of landslides and can be well applied to landslide susceptibility mapping. What needs to be emphasized is that the machine learning methods have the advantages of high efficiency, accurate prediction, time-saving, convenient implementation, which are relatively new and better evaluation models of susceptibility.