The benefits of computerized real-time river basin management in the Malahayu reservoir system
Bibliographic record
Abstract
This paper describes the developments of operating rules for the Malahayu reservoir system in Indonesia. The analysis in this study is based on the use of a simulation model with a nested network flow optimization subprogram, which required hydrologic time series of reservoir inflows as input data. Since estimates of historic naturalized flows were of insufficient length, they were used as a basis for developing a 100-year stochastic series which offered a more challenging input while preserving the relevant statistics of the original historic series. This study shows by how much the reservoir yield could have been increased in the past, assuming that short-term inflow and demand forecasts are available and that the proposed reservoir operating rule is obeyed. The increase is estimated by comparing the long- term average of the simulated diversions at the three weirs with the actual historic diversions which are on the record. A more efficient reservoir operating policy would increase the reservoir yield by 38% in the March-June period and by 33% in the July-October period. If additional local runoff into the weirs is utilized in the same period, the increased supply would range up to 66% in the March-June period and 43% in the July-October period. The results from this study provide a strong argument in favour of investing in modern technology as opposed to massive additional infrastructure development. Key words: linear programming, reservoir rule curve, simulation, optimization, stochastic inflow series.
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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 | 0.000 | 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) | 0.000 | 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".