Influence of Cumulative Rainfall on the Occurrence of Landslides in Korea
Bibliographic record
Abstract
This study presents the impact of cumulative rainfall on landslides, following the analysis of cumulative rainfall for 20 days before the landslide. For the 1520 landslides analyzed, the highest amount of average daily rainfall of 52.9mm occurred the day before the landslide, and the least amount of 6.1mm was experienced 20 days before the landslide. The least number of landslides (263 landslides) occurred when the cumulative rainfall is less than 20mm, and increased to 316 landslides in less than 30mm rainfall, 514 landslides in less than 80mm, 842 landslides in less than 150mm, and 678 landslides in 150mm and above. Considering the landslide occurrence in relation to the cumulative rainfall and the cumulative number of days, 986 landslides (64.9%) of the 1520 landslides were triggered by the 3 days cumulative rainfall for the 100mm rainfall and below, and 60% of landslides at the 5 days cumulative rainfall, indicating that the impact of cumulative rainfall on landslides was high in the 3 days and 5 days cumulative rainfall. More landslides occurred for the 101mm-200mm rainfall at the 10 days cumulative rainfall, more landslides for the 201mm-300mm rainfall at the 14 days cumulative rainfall, and more landslides for the 301mm-400mm rainfall at the 18 days cumulative rainfall. Three typologies of cumulative rainfall triggers are evident in Korea which includes: the early stacked rainfall accumulation type; the long-term intensive rainfall accumulation type; the continuous daily rainfall accumulation type. Cumulative rainfall is thus a major factor causing landslides. It is therefore imperative to take into consideration cumulative rainfall and the cumulative number of days as important triggers of landslides, as this could help contribute in landslide forecasting, thus putting in place measures to minimize the damage caused to life and property by landslides.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".