Building channel networks for flat regions in digital elevation models
Bibliographic record
Abstract
Abstract Digital elevation models (DEMs) are data sources for distributed rainfall–runoff modelling in terms of providing the channel network for a watershed of interest. Assigning flow directions over flat regions is an important issue in the field of DEM processing and extraction of drainage features. Existing methods cannot fully incorporate the information of known drainage features and terrain surrounding the flat region. This study presented a hydrological correction method that integrates topographic information from different sources to interpolate a convergent surface. It employs radial basis function interpolation to determine elevation increment at every position, utilizes data of digital channel network, incorporates elevation in the surrounding terrain, and ensures a convergent channel network while minimizing the impact of correction on the original DEM. The method can be easily implemented in geographic information system (GIS) environment. It was applied to the DEM of the Heshui Watershed, China. The extracted channel network was visually inspected and quantitatively assessed through analysing the flow direction raster. Results showed that the channel network generated by the hydrological correction was consistent with the known drainage features and contained less parallel channels comparing with the results from two existing methods. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".