GIS-based numerical modelling of debris flow motion across three-dimensional terrain
Bibliographic record
Abstract
Abstract The objective of this study is to incorporate a numerical model with GIS to simulate the movement, erosion and deposition of debris flow across the three dimensional complex terrain. In light of the importance of erosion and deposition processes during debris flow movement, no entrainment assumption is unreasonable. The numerical model considering these processes is used for simulating debris flow. Raster grid networks of a digital elevation model in GIS provide a uniform grid system to describe complex topography. As the raster grid can be used as the finite difference mesh, the numerical model is solved numerically using the Leap-frog finite difference method. Finally, the simulation results can be displayed by GIS easily and used to debris flow evaluation. To illustrate this approach, the proposed methodology is applied to the Yohutagawa debris flow that occurred on 20th October 2010, in Amami-Oshima area, Japan. The simulation results that reproduced the movement, erosion and deposition are in good agreement with the field investigation. The effectiveness of the dam in this real-case is also verified by this approach. Comparison with the results were simulated by other models, shows that the present coupled model is more rational and effective.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
GIS-coupled numerical model of debris flow; a geohazard modeling contribution.
This study develops and applies a debris-flow simulation model to a geological case.
GIS numerical modelling of debris-flow motion; geohazard engineering, not research about research.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".