Exploring Hydro-Meteorological Drivers of Landslide Displacement: A Time-Series Forecasting Approach Using LSTM at Ripley in British Columbia, Canada

Landslides are complex geohazards often driven by hydro-meteorological factors. Climate change is amplifying these drivers, potentially increasing landslide frequency and intensity. Addressing these challenges requires robust tools capable of capturing the dynamic interactions between hydro-mechanical processes. While physics-based models provide valuable insights, their reliance on simplifying assumptions limits their ability to fully represent these intricate systems. In contrast, deep learning techniques excel at uncovering non-linear interdependencies, making them well-suited for landslide modeling.

This study employs a Long Short-Term Memory (LSTM) neural network to forecast landslide displacements at the Ripley Landslide in British Columbia, Canada. Ripley is a translational landslide of significant geotechnical and environmental interest, primarily impacting major railway corridors and local river biodiversity. The landslide’s movements are influenced by a pre-sheared clay seam with residual friction angles of 9–16 degrees, as well as toe erosion and drawdown effects from the Thompson River during late spring.

Three GPS stations have monitored Ripley’s displacements since April 2008, consistently showing similar magnitudes and directions of movement. Data from one station were used to train the LSTM model, with river flow as the primary input. Synthetic noise levels were introduced into the data to evaluate model robustness, and a sensitivity analysis was conducted to examine the impact of different training datasets on displacement forecasts. Additional inputs, including temperature and precipitation, were incorporated to assess their contributions to model performance. Shapley values were employed to quantify the influence of each input variable, enhancing the explainability of the model that is typically obscured by the convoluted structure of neural networks.

This work demonstrates the potential of deep learning techniques to advance situational awareness and forecasting of landslide activity by leveraging hydro-meteorological drivers. The findings contribute to the development of data-driven approaches for landslide early warning systems and hazard mitigation strategies on a regional scale, as there are 11 other landslides in the valley within a 10-km distance that share similar surficial geology and exposure to hydro-meteorological drivers.