Evaluation of standard error of forecast of runoff volume using linear regression models
Bibliographic record
Abstract
Optimizing reservoir operations requires forecasts of seasonal inflow and a good understanding of the associated uncertainties. When forecasting seasonal runoff volume to a reservoir using a linear regression model, hydrologic forecasters typically use the standard error of residuals as the standard error of forecast to give water managers a sense of uncertainties in the forecast. However, this practice accounts for only the random error and ignores the modeling error in the volume forecast, resulting in underestimation of the standard error of the forecast. The underestimation can become significant in extreme runoff years for which reservoir operations tend to be most critical. This paper presents the algorithm for calculating the standard error of forecast, which takes into consideration both random and modeling errors. A simple way of calculating the standard error of forecast using built-in functions in Microsoft Excel is described. An example is used to demonstrate the potentially significant underestimation of the true error of a forecast if modeling error is ignored.Key words: standard error of forecast, residuals, runoff volume forecast, regression analysis.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".