Classification of Landslide Stability Based on Fine Topographic Features
Bibliographic record
Abstract
In this paper, we propose an extraction method for landslide topographic features and based on these fine features, we trained a landslide stability classification model. Topographic factors have a huge impact on the stability of landslides, and our new feature extraction methods can provide more accurate portraits of landslides. Considering the factors affected landslide stability, this paper studied the factors used in traditional quantitative stability calculations and analyzed the factors affecting stability based on landslide data. So, the proposed method is a combination of theoretical method and numerical method. Based on support vector classification (SVC), random forest (RF), and extreme gradient boosting (XGBoost) models, this paper compares and analyzes the rationality and reliability of the landslide stability classification results output by three models. After adopting the new fine feature extraction method, the accuracy of the stable classification results has been improved by about 5% on average, and the interpretability of the classification results has also been greatly enhanced.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".