Mathematical modeling for planning water-food-ecology-energy nexus system under uncertainty: A case study of the Aral Sea Basin
Bibliographic record
Abstract
A bi-level decentralized chance-constrained programming (BDCP) method is developed for planning water-food-ecology-energy (WFEE) nexus system. The BDCP method has advantages in balancing the tradeoff between two-level stakeholders in hierarchical structure and reflecting the synergy effect among multiple divisions under random uncertainty. Then, a BDCP-WFEE model is formulated for the Aral Sea Basin, where the upper-level model aims to maximize system benefit, and the multiple divisions at the lower-level model aim to maximize food production, ecological water allocation, and electricity generation. Compared with the conventional single-level model, results obtained from the BDCP-WFEE model under multiple scenarios reveal that (i) the food production would increase by 2.0%–3.6% , implying that the food demand of additional 0.7 million people can be met; (ii) the ecological water allocation would increase by 0.9%–3.0%, denoting that the amount of water to the Aral Sea would reach 23.4 km3 at the end of planning period; (iii) the electricity generation would increase by 5.4%–8.5%. Besides, under the premise of ensuring food security, the proportion of agricultural water allocation in the Aral Sea Basin would reduce by 17.0%, indicating that the BDCP-WFEE model can effectively optimize the water allocation pattern and alleviate the conflict of water resources allocation among competetive users. These findings can provide policy support for managers to solve the problems of water shortage, food crisis, ecological degradation, and electricity insecurity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".