Application and comparison of logistic regression model and neural network model in earthquake-induced landslides susceptibility mapping at mountainous region, China

Abstract

The main objective of this study is to evaluate the performances of different earthquake-induced landslides susceptibility mapping models at mountainous regions in China. At first, 160 earthquake-induced landslide points were identified from field investigations. Concurrently, based on the results of a literature review and the field investigation, 12 influencing factors were considered, and the corresponding thematic layers were generated using geographic information system (GIS) technology. Subsequently, 20 groups with a fixed number of cells were collected as a common training dataset for the two different models, based on a random selection from the entire database (including landslide cells and no-landslide cells). The neural network (NN) model and logistic regression (LR) model were developed with R software. Finally, earthquake-induced landslides susceptibility maps of Wenchuan county were produced, very low, low, medium, high and very high susceptibility zones cover. The validation results indicate that the landslide data from field investigations are in good agreement with the evaluation results, and the LR model has a slightly better prediction than the NN model in this case. In general, the NN model and LR models are satisfactory for susceptibility mapping of earthquake-induced landslides at mountainous regions.

Keywords

• Mountainous region
• earthquake-induced landslides
• susceptibility mapping
• logistic regression model
• neural network model