Slope stability prediction using multi-stage machine learning with multi-source data integration strategy
Bibliographic record
Abstract
Slope stability analysis is a crucial task in geotechnical engineering. Machine learning (ML) has been widely used for slope stability analysis based on inference relationships learned from training data. However, the performance of ML is constrained since available data from historical slopes is limited. The multi-source training dataset is generated in this study, including data from historical slopes, numerical simulations, and physical experiments. The multi-stage machine learning (MSML) model is proposed to utilize training data from different sources. We select stability status, factor of safety, and probability of slope stability as outputs, and design the hybrid loss function consisting of data-based and knowledge-based terms to further improve the performance of the MSML model based on the relationships among three outputs. The results of the proposed MSML model are then compared with the baseline ML methods using evaluation metrics. The comparisons demonstrate that the proposed MSML model outperforms all baseline ML methods with the highest accuracy (0.893), precision (0.845), recall (0.907), and F1-score (0.868). Even the proposed pre-trained model achieves performance similar to most baseline models. Finally, a further comparison using five landslide cases demonstrates that our proposed method provides accurate and reliable prediction results with excellent generalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".