Application of fuzzy logic to forecast seasonal runoff
Bibliographic record
Abstract
Abstract Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed. In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed ‘linguistically’ into a formal analysis. For example, ‘high snowpack’, ‘average snowpack’ and ‘low snowpack’ became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: ‘low’, ‘average’ and ‘high’. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".