Output updating of a physically based model for gauged and ungauged sites of the Upper Thames River watershed
Bibliographic record
Abstract
Abstract This study introduces a new ANN updating procedure of streamflow prediction for a physically based HEC-HMS hydrological model of the Upper Thames River watershed (Ontario, Canada). Besides streamflow and precipitation, the updating procedure uses other meteorological variables as inputs, which are not applied in calibration of the HEC-HMS model. All the results of performance measures on training, validation and test datasets for river gauges at Mitchell and Stratford revealed that the ANN updated models have performed better than the HEC-HMS model. The ANN model results were in excellent agreement with observed streamflow. The uncertainties can be associated with different input variables and different length of datasets used in the HEC-HMS model and the ANN model. The performance results suggest improvement in the RMSE values of the trained networks when additional meteorological data was used. The updated errors from the gauged sites of Mitchell and Stratford were used to update the streamflow values at the ungauged site of JR750 of the HEC-HMS model. While the underlying physical process in the ANN model consisting of interconnected neurons to map input-output relationships is not easily understood (in a form of mathematical equation), the HEC-HMS hydrological model can reveal useful information about the parameters of a hydrological process.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".