Debris Flow Hazard Mapping Along Linear Infrastructure: An Agent Based Model and GIS Approach
Bibliographic record
Abstract
Often linear infrastructure, including rail, highways, and pipelines, span large geographic areas intersecting a variety of terrain, predisposing infrastructure to a higher likelihood of geohazard interaction. Debris flow models can be particularly advantageous in remote hazard and risk mapping along linear infrastructure as runout from susceptible slopes may extend considerable distance downslope to a receptor. In this sentiment, a method is developed using an agent-based model, DebrisFlow Predictor, in combination with geographic information software (GIS), to produce regional debris flow hazard and risk profiles along widespread corridors. Thousands of debris flows upslope of a receptor(s) are simulated in the model environment (i.e., model scenarios). Outputs of the modelled scenarios provide probabilistic spatial attributes of debris flow runout and depth across a digital elevation model at a 5m resolution. The outputs of multiple scenarios are mosaiced and corrected to in-situ temporal and spatial debris flow initiation conditions in GIS. The corrected scenario outputs provide a comprehensive hazard profile along the infrastructure alignment, that in turn can facilitate quantified vulnerability and risk calculations. Thousands of modelled debris flows throughout several physiographic regions of Canada and the United States of America, calibrated to local conditions, provide substantive support for a novel methodology to identify key hazard and risk locations to major linear infrastructure.
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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".