Integrating physical and empirical landslide susceptibility models using generalized additive models

Physically based models are commonly used as an integral step in landslide hazard assessment. Geomorphic principles can be applied to a broad area, resulting in first order assessment of landslide susceptibility. New techniques are now available that may result in the increased accuracy of such models. We investigate the possibility to enhance landslide susceptibility modeling by integrating two physically-based landslide models, the Factor of Safety (FS) and the Shallow Stability model (SHALSTAB), with traditional empirical-statistical methods that utilize terrain attribute information derived from a digital elevation model and land use characteristics related to forest harvesting. The model performance is measured by the area under the receiver operating characteristic curve (AUROC) and sensitivity at 90% and 80% specificity both estimated by bootstrap resampling. Our study examines 278 landslide initiation points in the Klanawa Watershed located on Vancouver Island, British Columbia, Canada. We use a generalized additive model (GAM) and a logistic regression model (GLM) combining physical landslide models, terrain attributes and land use data, and GAMs and GLMs using only subsets of these variables. In this study, all empirical and combined physical-empirical models outperform the physically-based models, with GAMs often performing significantly better than GLMs. The strongest predictive performance is achieved by the GAMs using terrain attributes in combination with land use data. Variables representing physically-based models do not significantly improve the empirical models, but they may allow for a better physical interpretation of empirical models. Also, based on bootstrap variable-selection frequencies, land use data, FS, slope and plan/profile curvature are relatively the most important predictor variables.

Research Highlights► We integrated physically-based slope stability models with empirical-statistical methods. ► A non-linear regression technique is applied with digital terrain analysis. ► Non-linear models provide marginal improvements over traditional linear models. ► Empirical and combined models outperform physically-based landslide models. ► Forestry related land-use variables provide significant model improvements.