Beyond boundaries: AI-optimized global landslide susceptibility mapping
Bibliographic record
Abstract
Landslides pose a significant global threat, causing extensive loss of life, economic damage and environmental degradation. Despite advancements in landslide susceptibility mapping, existing methods often lack global-scale applicability and fail to incorporate robust optimization strategies for improved predictive accuracy. This study addresses these gaps by developing an optimized framework using support vector regression (SVR) enhanced with meta-heuristic algorithms (grey wolf optimizer [GWO] and bat algorithm) to refine model hyper-parameters. It integrates a globally representative data set of 37,984 landslide and non-landslide locations, ensuring broader applicability and generalizability. The information gain ratio method assessed the relative importance of 12 geo-environmental factors influencing landslide. The results indicated that all models achieved good predictive performance during the testing phase, as evidenced by an area under the receiver operating characteristic curve (AUC) value exceeding 0.8, but the SVR-GWO model exhibited the highest prediction accuracy (AUC = 0.92), making it suitable for large-scale hazard assessment. Plan curvature emerged as the most influential factor, surpassing slope, land use, and rainfall that are dominant at regional or local scales. The five countries with the highest landslide-prone areas were Russia, Canada, USA, China, and Brazil. The results support policymakers and urban planners in developing efficient strategies to minimize landslide risks.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".