A Water Footprint Based Hydro-Economic Model for Minimizing the Blue Water to Green Water Ratio in the Zarrinehrud River-Basin in Iran
Bibliographic record
Abstract
The efficient use of water should involve decisions for balancing green water (GW) and blue water (BW) use for sustainable development. More specifically, the focus of irrigation water management should be redirected from a BW perspective toward considering the full water balance, including GW flow. This study presents a modelling approach in a system dynamic platform for minimizing the BW to GW ratio in a water basin while maximizing total agricultural profit. The paper considers the compromise between any reduction in the GW to BW ratio and the possible changes in the economic achievement of the region through varying land use and cropping patterns. This paper explores and presents the possibilities of reducing the BW to GW ratio in the Zarrinehrud River-basin for moderate, dry, and wet years using the water footprint concept. Results show that under all combinations of economic objective and BW to GW ratio addressed by water footprint measures, the hydro-economic performance of the river basin may substantially be improved as compared with the current practice. Either weights may systematically be changed or multiple objective optimization algorithms may be employed if a more precise tradeoff between the objectives is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".