Solving a stochastic reservoir management problem with multilag autocorrelated inflows
Bibliographic record
Abstract
There are many advantages in taking account of multilag autocorrelation of inflows in a reservoir management problem: The flood and water shortage risks diminish, there are fewer spillages, and the generation of hydroplants downstream from the reservoir increases. The disadvantage is that the number of state variables in the optimization problem increases with the number of lags. This paper shows that it is possible to adequately represent the multilag autocorrelation by a single hydrologic variable, the value of which changes from day to day and is equal to the conditional mean of the daily inflow. The paper also shows how to determine the probability distribution of the hydrologic variable for one day as a function of the hydrologic variable and the inflow of the preceding day. This is done for cases where the inflows are represented by a linear autoregressive (AR) model and linear autoregressive‐moving‐average (ARMA) model. The reservoir management problem is solved with stochastic dynamic programming (SDP). Numerical results are presented.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".