Post Seismic Debris Flow Modelling Using Flo-2D; Case Study of Yingxiu, Sichuan Pronvince, China
Bibliographic record
Abstract
In many parts of the world debris flows are one of the most dangerous of all mass wasting events. Mountainous areas with high slope instability, high seismic activities and extreme rainfall condition are the main triggering factors. The Monday, May 12, 2008, mega-earthquake of magnitude 8.0 that struck the Wenchuan area, Northwestern Sichuan province in China was catastrophic. This event, led to co-seismic landslides and subsequent rainfall induced debris flow in Yingxiu catchment on August 14th, 2010. The catchment has a very steep topography, an area of 5.35 km2 and a channel length of 3.55 km. The aim of this research is to model the post seismic debris flow, Parameterize and calibrate the event. Two main initiation zones were identified based on susceptibility assessed from geomorhological mapping and formed the bases for input in the FLO-2D model. 161, 350 m3 (64.54%) of the debris flow volume was modeled with FLO-2D in a manner consistent within the limit of the data available. FLO-2D model do not incorporate entrainment of materials in the transport zone. Thus, limitation to the production of the total deposits volume on the debris flow fan. The model was parameterized and the result shows that, Sediment concentration and the coefficient of friction were the two main parameters that affected the velocity of debris flow, area of inundation and the impact force respectively. Finally, the debris flow was calibrated using a back analysis of the debris flow event of 2010.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".