A multi-source landslide early warning model based on dynamic monitoring data and the SAAHP-FCE method
Bibliographic record
Abstract
Landslide early warning is crucial for mitigating disaster impacts but remains challenging due to numerous influencing factors, resulting in untimely or unreliable warnings. To address these issues, a multi-source early warning model using dynamic monitoring data is proposed, combining the Self-Adaptive Analytic Hierarchy Process (SAAHP) with the Fuzzy Comprehensive Evaluation (FCE) method. First, a hierarchical structure and quantitative thresholds are established based on the landslide mechanism and monitoring data, classifying warnings into four levels: safety, caution, vigilance, and alarm. SAAHP is then applied to calculate evaluation factor weights using a judgment matrix. Finally, FCE evaluates the overall impact of these factors, providing warning levels, probabilities, and scores. An experiment on the Heifangtai DC#7 landslide demonstrated the model’s success in capturing all four events in this disaster. The ‘alarm’ warning was issued two days earlier than traditional velocity-based models. Similarly, for the Xinpu landslide in the Three Gorges Reservoir area, the model integrated multi-station monitoring data, reducing the impact of data interruptions on warning accuracy. This approach effectively identifies landslide risks in advance, providing comprehensive, dynamic warnings and overcoming the limitations of single-indicator models. It offers a reliable framework for improving early warning precision and disaster preparedness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".