An improved method to predict man-made slope failure using machine learning tools
Bibliographic record
Abstract
Landslide hazards associated with man-made slopes are increasing due to ageing and extreme weather conditions under a changing climate. To effectively mitigate landslide risks, the implementation of regional landslide early warning systems is desirable, albeit challenging, if not impossible, due to the scarcity of reliable landslide data and suitable predictive tools. In this paper, a thorough analysis has been conducted on reasonably reliable and substantial amounts of historical rainfall data, slope features, and landslide inventory of man-made slope failures in Hong Kong. Four different machine learning methods, namely logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost), have been employed. The predicted number of landslides from the machine learning methods is compared with the predictions made by the current Landslip Warning System in Hong Kong. The effects of rainfall parameters and slope features on model performance are also investigated. The analysed results show that dynamic rainfall conditions are identified as the most influential factors for predicting man-made slope landslide. A combination of 1 and 12 h maximal rolling rainfall (MRR) demonstrates superior performance compared to relying solely on the 24 h MRR. Therefore, this combination is recommended for predicting man-made slope failures in Hong Kong.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".