Exploring Hydro-Meteorological Drivers of Landslide Displacement: A Time-Series Forecasting Approach Using LSTM at Ripley in British Columbia, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".