Evaluating the Environmental Impacts of Water Distribution Systems by Using EIO-LCA-Based Multiobjective Optimization
Bibliographic record
Abstract
Climate change has made environmental impact a factor of growing importance in decision making for municipalities. Increasingly, the environmental impacts of expanding and operating a water distribution system (WDS) are considered alongside the cost and hydraulic design. This paper presents a nondominated sorting genetic algorithm (NSGA-II) that minimizes capital costs, annual pumping energy use, and environmental impacts in WDS design that adheres to hydraulic constraints. A previously developed environmental impact (EI) index is included in the environmental objective function of the optimization program. The EI index normalizes and aggregates multiple environmental measures evaluated with an economic input-output life-cycle assessment (EIO-LCA) model. The EIO-LCA-based NSGA-II was applied to the Anytown network. Annual pumping energy use was found to dominate the EI index while capital cost and the EI index were inversely related, and the annual pumping energy use and the EI index followed a near linear relationship. The location and shape of the Pareto fronts were sensitive to demand and roughness coefficient (C-factor) adjustments with greater sensitivity observed for changes in demand than changes in the C-factor.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".