Performance Evaluation of Artificial Neural Networks for Runoff Prediction
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
Spring runoff prediction in the Red River Valley, southern Manitoba, Canada, is an important issue because of the devastating effect of the flood of 1997 in that area. Increasing the accuracy of the prediction process is a practical necessity. This study looks at the artificial neural networks (ANN) technique and compares it to linear and nonlinear regression techniques. The advantages and disadvantages of the three modeling techniques are discussed. To fill the predictive accuracy evaluation gap left by the mean squared error and the mean relative error, a modified statistic, namely, pooled mean squared error, is developed and explained. The aim of this work is to show the applicability of ANN for runoff prediction and to evaluate their performances by comparing them with traditional techniques. In this study, according to the accuracy of results, the ANN models show superiority in most of the cases. However, in some situations, the performance of the other two techniques was comparable.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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 it