A Non-Deterministic Integrated Optimization Model with Risk Measure for Identifying Water Resources Management Strategy
Bibliographic record
Abstract
Water resources system planning often exhibits high modeling error and uncertainty. Uncertainty in system parameters as well as their interrelationships can strengthen the conflict-laden issue of water allocation among competing interests. In this study, a non-deterministic integrated optimization model with risk measure is developed for planning water resources management. It can (i) deal with complex uncertainties described as probability distributions, fuzzy sets, and their combinations, (ii) provide an effective linkage between the predefined policies and the associated economic implications, and (iii) reflect policymakers’ preferences to the tradeoff between system benefit and economic loss. The developed model is then applied to planning water resources allocation of the Heshui River Basin (China), where 960 scenarios are analyzed under various uncertainty and risk measures. Results disclose that (i) not only uncertainties of fuzziness and randomness but also risk attitudes of decision makers have significant impacts on water-allocation scheme and system benefit; (ii) the selection of a suitable alternative among solutions under different α , μ and λ values is complicated; (iii) water shortage would occur when water availability is less than the promised target; (iv) agriculture would encounter most serious scarcity compared to municipal and industry; (v) the conflict between economic development and agricultural sustainability would be a challenged issue that would enforce the local authority to adjust water-allocation policy. The findings can provide superior fundamental understanding of the study basin to improve water-allocation decisions under complex uncertain condition.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 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".