A Landslide Susceptibility Evaluation of Highway Disasters Based on the Frequency Ratio Coupling Model

A landslide disaster, especially a highway landslide, may greatly impact the transport capacity of nearby roads. Keeping highways open, in particular, is crucial for supporting the functioning of the economy, society and people. Therefore, evaluating the highway landslide susceptibility is particularly important. In this paper, the city of Laibin, in the Guangxi Zhuang Autonomous Region of China, was taken as the study zone. According to data on 641 highway landslide disaster points measured in the field and a basic evaluation of the study area, nine evaluation factors-the elevation, slope, aspect, height difference, plan curve, profile curve, precipitation, Topographic Wetness Index (TWI) and vegetation coverage-were selected. We coupled a Frequency Ratio (FR) model, Analytic Hierarchy Process (AHP), Logistic Regression (LR), Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) to evaluate the susceptibility to highway landslides, with a Receiver Operating Characteristic (ROC) curve used to analyze the precision of these models. The ROC curve showed that the accuracy of the five models was greater than 0.700 and thus had a certain reliability. Among them, the FR-LR model had the highest accuracy, at 0.804. The study protocol presented here can therefore provide a reference for evaluation studies on landslide susceptibility in other areas.

Automated summary

Evaluating the susceptibility to highway landslides is crucial for keeping highways open. In this study, the city of Laibin in China was chosen as the study area, and nine evaluation factors were selected. Multiple models, including FR, AHP, LR, BPNN, and SVM, were used to evaluate the susceptibility. The results showed that the FR-LR model had the highest accuracy.