Forecasting groundwater level for soil landslide based on a dynamic model and landslide evolution pattern

Abstract

The occurrence and migration of groundwater is a key natural factor that directly affects the stability of landslides, and rainfall has a large effect on the groundwater level in soil landslides. This study used the Baijiabao landslide in the Three Georges area of China as the research subject and used a combination of more than seven years of rainfall and GPS (Global Positioning System) monitoring data from 2007 to 2013. We applied the K-means clustering method to classify one cycle of the evolution stage into three classes based on the relative displacement of the main sliding surface of the landslide. To illustrate the relationship between the three landslide evolution divisions and the dynamic indicators, we identified rainfall factors that correspond to the actual change in the landslide using the minimal description length principle method. Based on the relationship between the actual deformation stage of the landslide and the rainfall factor from historical monitoring, the mean absolute error of the dynamic exponential smoothing model was 0.053, and the correlation coefficient was 0.929. The size of the smoothness index could be modified in real-time to achieve dynamic correction, which indicates that the model exhibited high reliability and confirmed the usefulness of the proposed model for forecasting groundwater level changes based on deep-seated soil landslide type.