Relationship between Vegetation and Landslide Depth Using Statistical Methods: Aso Region, Kumamoto Prefecture, Japan
Bibliographic record
Abstract
In recent years, climate change has led to an increased frequency and scale of heavy rainfall events, and subsequently, a rise in the occurrence of landslide induced by these rainfall events has been observed.Vegetation enhances slope stability by increasing soil strength through root systems.It is essential to quantitatively assess the influences that vegetation exerts on the occurrence of landslides and their depth to utilize vegetation to reduce disaster risk.Hence, in this study, we aimed to quantitatively evaluate the effects of vegetation diversity on the depth of landslides in the Aso region of Kumamoto Prefecture using statistical methods.We collected necessary data on topography, geology, vegetation, and rainfall and analysed them using a random forest.As a result of constructing the RF, the factors importance was the slope angle was the largest, followed by the landslide area, and the importance of vegetation was not large.As a result of creating partial dependence plots of the average landslide depth for each geology and vegetation type, the average landslide depth of secondary grasslands was approximately 20 cm smaller than that of broadleaf forests in all geological categories.This study could contribute substantially to future disaster mitigation efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".