Towards a generic neural network model for the prediction of daily streamflow in ungauged boreal plain watersheds
Bibliographic record
Abstract
Most of the currently available streamflow neural network models are either recurrent neural networks or feed-forward multi-layer perceptron (FF-MLP) requiring past flow values for lead time prediction. These models cannot be used for modelling ungauged watersheds because past flow values are not available in such cases. This study proposes a FF-MLP algorithm that relies only on low-cost, readily available meteorological data and careful time series manipulation prior to model building, and thus, is suitable for modelling streamflow in ungauged watersheds. The proposed approach was tested on four watersheds (5 to 130 km2) in the Canadian Boreal forest and was found to provide an efficient modelling alternative for daily streamflow predictions. To assess the possibility of successful model transferability from a gauged watershed to a hydrologically similar ungauged watershed, a new remotely sensed hydrologic similarity measure — SWMIR_SI — was proposed and was found to provide a successful indicator of basin similarity. The square of Pearson’s correlation coefficient, r2, was evaluated to exceed 0.71 when SWMIR_SI was regressed to models’ “goodness-of-fit” statistics reflecting the usefulness of the approach.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".