Landslide spatial prediction based on cascade forest and stacking ensemble learning algorithm
Bibliographic record
Abstract
Landslides are a major threat to the safety of human life and property. The purpose of landslide spatial prediction is to establish the relationship between the location of landslides and each landslide evaluation factor, and to spatially identify high landslide risk areas using data mining and geographic information science. In this paper, a landslide spatial prediction model is put forward based on cascade forest (CF) and Stacking ensemble learning algorithm. Firstly, the landslide spatial prediction scheme is designed. Then, the improved CF is established by combining random forest (RF) and extreme gradient boosting (XGBoost). The Stacking ensemble learning algorithm is introduced to establish CF-Stacking model combined with the improved CF. Finally, experiments are conducted using geospatial data of the actual study area. 12 landslide disaster-inducing factors are extracted from the study area, and the CF-Stacking model is applied to the spatial prediction of landslides. The result shows that CF-Stacking outperforms comparative models in terms of the area under curve and brier score, demonstrating its effectiveness in predicting landslide spatial patterns. The CF-Stacking model is used to generate a landslide susceptibility map for Fengjie, which provides valuable guidance for geological hazard early warning.
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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 | 0.001 | 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".