Regional monthly runoff forecast in southern Canada using ANN, <i>K</i>-means, and <i>L</i>-moments techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it