A Hydroinformatic Approach to Assess Interpolation Techniques in High Spatial and Temporal Resolution
Bibliographic record
Abstract
One of the problems which often arises in hydrologic and hydraulic design is the estimation of data at a given site, where data are missing or the site is ungauged. Estimates can be made by spatial interpolation of data available at other sites. Geographic information systems offer a number of embedded ready-to-use spatial interpolation techniques. It is the intent of this paper to document the development of a GIS-based tool to compare the applicability of various interpolation techniques for estimating rainfall for a selected area in Ontario, Canada. The interpolation techniques in the ArcView GIS® are: IDW, Spline (Tension and Regularized), 2nd Order Polynomial, and Kriging (Ordinary and Universal). One-minute rainfall data for sixteen events were extracted from ten rainy days during the summer of 1989. The test case is an 8 km by 8 km area located in the Hamilton-Wentworth region. It is covered by a nine-gauge network. The comparison shows that the IDW and Kriging_Gaussian are the best and worst techniques, respectively. Results also point out that some gauges are more important than others. The results, however, can also be considered a useful warning of the instability of interpolation techniques with limited/sparse data, particularly in the context of commercial software.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".