Comparison of Two Data-Driven Streamflow Forecast Approaches in an Adaptive Optimal Reservoir Operation Model
Bibliographic record
Abstract
This study investigates the effect of two data-driven inflow prediction methods on the performance of a proposed adaptive real-time optimum reservoir operation model. The model consists of three modules; a forecasting module, which predicts the monthly future inflows, a reservoir operation optimization module, determining monthly optimum reservoir releases up to the end of a year, and an updating module, updating the current state of the system and provides the other two modules with the latest observed information on future inflows. K-nearest neighbor (KNN) and adaptive neuro- fuzzy inference system (ANFIS) approaches are used to forecast monthly inflows to the reservoir. The results demonstrate that ANFIS outperforms the KNN approach by 25, 23, 27 and 10 percent with respect to RMSE, PWRMSE, NSCE and correlation coefficient indices, respectively. However, the objective function values of the reservoir operation optimization model associated with each of those forecast models reveal that ANFIS-based adaptive reservoir operation model is only 5% better than the KNN-based model. This observation highlights the significance role of adaptation and updating procedure in the reduction of streamflow forecast errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".