Deformation evaluation and displacement forecasting of baishuihe landslide after stabilization based on continuous wavelet transform and deep learning

Yuting Liu, Giordano Teza, Lorenzo Nava, Zhilu Chang, Min Shang (), Debing Xiong and Simonetta Cola
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Yuting Liu: Ministry of Education
Giordano Teza: Alma Mater Studiorum University of Bologna
Lorenzo Nava: University of Padua
Zhilu Chang: University of Padua
Min Shang: Ministry of Education
Debing Xiong: SGIDI Engineering Consulting (Group) Co., Ltd
Simonetta Cola: University of Padua

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 11, No 11, 9649-9673

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 implemented since 2003. However, after 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 interventions led to an effective stabilization improvement since the present displacement rate is smaller than that 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-step procedure is proposed here. 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 one piezometer and reservoir water level (RWL). 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 using 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 interventions, rainfall is an important factor inducing deformation acceleration. The hydrodynamic pressure induced by the difference between the groundwater 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.

Keywords: Landslide displacement forecast; Reservoir water level; Rainfall; Deep learning; Discrete wavelet transform; Continuous wavelet transform; Convolutional neural network (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06580-7

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