Deformation Evaluation and Displacement Forecasting of Baishuihe Landslide after Stabilization based on Continuous Wavelet Transform and Deep Learning

Abstract

Abstract Baishuihe Landslide is a large active landslide that threatens shipping transportation in the Three Gorges Reservoir (China). A manual monitoring system has been active since 2003. However, after the realization of some intervention works in 2018-2019, new automatic instruments providing continuous data on displacements, rainfall, reservoir water level, and groundwater table were installed. The data recorded by the new system show that these works led to an effective stabilization improvement since the present displacement rate is lower than that detected before interventions. However, the relevance of the Three Gorges basin and the potential hazard of a possible collapse requires a reliable forecast of the landslide evolution in a time scale from a few hours to a few days. To this aim, a two steps procedure is here proposed. In the first step, after a preliminary preprocessing-denoising of data, carried out by means of Discrete Wavelet Transform (DWT), a Continuous Wavelet Transform (CWT) procedure is used to provide scalograms of the time series of three quantities, e.g., landslide displacement rate, rainfall and the difference of water level between the piezometer and reservoir water level. In the second step, to evaluate the relationships among the velocity trend and the other significant quantities and obtain a reliable velocity forecast, the images given by binding together two or three scalograms of the mentioned quantities were analyzed with a Convolutional Neural Network (CNN) tool. Several trials with different combinations of input time series of 2 or 3 quantities were carried out in order to recognize the factors which mainly affect the current displacement evolution. The results show that, after the works, rainfall is an important factor inducing deformation acceleration. The hydrodynamic pressure induced by the difference between the ground water pressure and reservoir water level also plays a dominant role in accelerating the Baishuihe landslide. Furthermore, the coupling of rainfall and hydrodynamic pressure produces displacement velocities higher than what the quantities singularly do. These results provide valuable indications for optimizing the monitoring configuration on the landslide and obtaining velocity forecasts in a few hours/days.