GIS-based Modeling of Debris Flow Runout Susceptibility in Kulekhani Watershed, Nepal
Bibliographic record
Abstract
Rainfall-induced landslide masses often change into disastrous debris flows and damage large areas in Nepal's mountainous region. The area covered by debris flow inundation is a most essential component for landslide hazard assessments leading to development of land use plans for saving lives and property. Because debris flow is a complex natural phenomenon, runout analysis requires very detailed information and rigorous procedures. Various empirical and dynamic models are available for debris flow runout simulation. However, a simple and publicly accessible model that can provide reasonable results is the ideal option for engineers and scientists. The Flow-R model with various algorithms has the capability to analyze debris flow inundation with limited input information, and the model software is readily available in the public domain. In this research, the Flow-R model with user-defined landslidesusceptible areas for a single rainfall event (540 mm in 24 hours) was chosen for debris flow runout analysis in Kulekhani Watershed (Nepal). The results obtained from this modeling for the debris flow inundation area was 2.68% of the watershed. The result is comparable to previously observed debris flow area in the study watershed.
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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 | 0.000 | 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".