Improving operating policies of large‐scale surface‐groundwater systems through stochastic programming
Bibliographic record
Abstract
Abstract The management of large‐scale water resource systems with surface and groundwater resources requires considering stream‐aquifer interactions. Optimization models applied to large‐scale systems have either employed deterministic optimization (with perfect foreknowledge of future inflows, which hinders their applicability to real‐life operations) or stochastic programming (in which stream‐aquifer interaction is often neglected due to the computational burden associated with these methods). In this paper, stream‐aquifer interaction is integrated in a stochastic programming framework by combining the Stochastic Dual Dynamic Programming (SDDP) optimization algorithm with the Embedded Multireservoir Model (EMM). The resulting extension of the SDDP algorithm, named Combined Surface‐Groundwater SDDP (CSG‐SDDP), is able to properly represent the stream‐aquifer interaction within stochastic optimization models of large‐scale surface‐groundwater resource systems. The algorithm is applied to build a hydroeconomic model for the Jucar River Basin (Spain), in which stream‐aquifer interactions are essential to the characterization of water resources. Besides the uncertainties regarding the economic characterization of the demand functions, the results show that the economic efficiency of the operating policies under the current system can be improved by better management of groundwater and surface resources.
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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 | 0.000 | 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".