Groundwater Level Prediction for Landslides Using an Improved TANK Model Based on Big Data

Abstract

Geological conditions and rainfall intensity are two primary factors that can induce changes in groundwater level, which are one of the major triggering causes of geological disasters, such as collapse, landslides, and debris flow. In view of this, an improved TANK model is developed based on the influence of rainfall intensity, terrain, and geological conditions on the groundwater level in order to effectively predict the groundwater level evolution of rainfall landslides. A trapezoidal structure is used instead of the traditional rectangular structure to define the nonlinear change in a water level section to accurately estimate the storage of groundwater in rainfall landslides. Furthermore, big data are used to extract effective features from large-scale monitoring data. Here, we build prediction models to accurately predict changes in groundwater levels. Monitoring data of the Taziping landslide are taken as the reference for the study. The simulation results of the traditional TANK model and the improved TANK model are compared with the actual monitoring data, which proves that the improved TANK model can effectively simulate the changing trend in the groundwater level with rainfall. The study can provide a reliable basis for predicting and evaluating the change in the groundwater state in rainfall-type landslides.