A deep learning-based model for endorsing predictive accuracies of landslide prediction: insights into soil moisture dynamics
Bibliographic record
Abstract
Abstract Introduction and Research Gap This study presents a comprehensive framework for predicting volumetric water content (VWC) to mitigate shallow, rainfall-induced landslides, bridging existing gaps in the literature. Methodology The framework synergistically integrates the empirical strengths of deep learning (DL) with the physical dynamics of the VWC subsurface behavior. Statistical, shallow machine learning (ML), and DL models were investigated with optimization techniques and sensitivity analyses to establish benchmarks for comparison and derive optimal predictions. DL and probability theory enable both point and interval predictions. Findings Validation on the Pa Mei landslide demonstrates strong performance with mean absolute errors (MAE) ranging from 0.35% to 1.22% and Predicted Interval Coverage Probabilities (PICP) from 0.86 to 0.91. Predicted VWC deviations were propagated into Factor of Safety (FOS) calculations, yielding robust performance metrics with R 2 and PICP of 0.89 and 0.85, respectively. Transferability is demonstrated at the Tung Chung landslide, where MAE ranges from 0.36% to 1.25% and PICP from 0.86 to 0.95. Significance This framework demonstrates improved accuracy and introduces a practical data-sharing mechanism to address monitoring challenges such as power consumption and data loss, offering a robust tool for hazard mitigation and decision support.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".