Probabilistic evaluation of loess landslide impact using multivariate model

The Loess Plateau is the largest loess accumulation area around the world, onto which loess landslides have been occurring frequently each year, thus bringing significant threats to communities there. To mitigate or manage the risk brought by loess landslides, many methods have been developed to gain insights into mechanisms that trigger loess landslides, or to identify regions that are susceptible to landslides through landslide susceptibility mapping. However, none of these methods can be used to quantitatively evaluate possible impact of potentially unstable slopes, which offers important information for risk management, especially for regions with high susceptibility to landslides. This study aims to fill this gap by constructing a loess landslide database first from field investigation. Then, a multivariate model for loess landslide data, including its height, width, area, and length, is developed considering correlation among these parameters. Subsequently, the multivariate model is used to predict statistically and quantitatively impact of a potentially unstable loess slope, in terms of slide width, length, and area, given height of the potentially unstable loess slope. The proposed method is applied to loess landslides occurred in Baoji City for illustration. Results show that the proposed method works reasonably well. In addition, some key equations are provided using results from the multivariate model. With these equations, geotechnical engineers or decision-makers can evaluate possible impact of a potentially unstable loess slope with minimal effort.