Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization
Bibliographic record
Abstract
ABSTRACT Regional-scale assessments for debris-flow and debris-flood propagation and avulsion on fans can be challenging. Geomorphological mapping based on aerial or satellite imagery requires substantial field verification effort. Surface evidence of past events may be obfuscated by development or obscured by repeat erosion or debris inundation, and trenching may be required to record the sedimentary architecture and date past events. This paper evaluates a methodology for debris-flow and debris-flood susceptibility mapping at regional scale based on a combination of digital elevation model (DEM) metrics to identify potential debris source zones and flow propagation modeling using the Flow-R code that is calibrated through comparison to mapped alluvial fans. The DEM metrics enable semi-automated identification and preliminary, process-based classification of streams prone to debris flow and debris flood. Flow-R is a susceptibility mapping tool that models potential flow inundation based on a combination of spreading and runout algorithms considering DEM topography and empirical propagation parameters. The methodology is first evaluated at locations where debris-flow and debris-flood hazards have been previously assessed based on field mapping and detailed numerical modeling. It is then applied over a 125,000 km2 area in southern British Columbia, Canada. The motivation for the application of this methodology is that it represents an objective and repeatable approach to susceptibility mapping, which can be integrated in a debris-flow and debris-flood risk prioritization framework at regional scale to support risk management decisions.
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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".