Regional monthly runoff forecast in southern Canada using ANN, <i>K</i>-means, and <i>L</i>-moments techniques
Bibliographic record
Abstract
River runoff forecasting is necessary for numerous applications related to water use, including water supply management, power generation and flooding protection measures. In this study, a regional model using an artificial neural network (ANN) is proposed for monthly runoff forecasting, which considers stations linked to the network that belong to the same homogeneous region, and are delimited using K-means (KM-ANN) and L-moments (LM-ANN) techniques. This methodology was applied to a sample of 90 monthly runoff series in southern Canada. The results were compared to those of a traditional neural network for a given site (ANNs) using statistical indices, such as root-mean-squared error (RMSE), relative square error (RSE), mean absolute error (MAE), relative absolute error (RAE), the concordance index (d) and the coefficient of determination (r2). The LM-ANN technique produced better forecasts in 56.7% of the analysed stations, whereas the KM-ANN and ANN techniques produced better forecasts in 27.7% and 15.6% of the stations, respectively. Thus, the results indicate that the regionalisation process improved the forecasts in 84.4% of the studied cases, and the estimation uncertainty was reduced by an average of 31.8%, according to the RMSE, RSE, MAE and RAE values. Therefore, its application is recommended in Canada, where it would be useful for the Integrated Water Resources Management Program.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".