Optimization of pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty
Bibliographic record
Abstract
Numerical models and optimization algorithms can be valuable tools for decision-making in coastal and island aquifers, where pumping wells are threatened by salinization. Yet, the implementation of pumping optimization under uncertainty remains limited in practice, because of long simulation times and challenges associated with uncertainty propagation through series of models. A method was developed to optimize pumping rates in an island freshwater lens considering parameter, observation, and climate uncertainty. It was implemented in an island aquifer in the Magdalen Islands (Québec, Canada). A seawater intrusion model with rapid simulation times was developed using MODFLOW-SWI2. The iterative ensemble smoother algorithm implemented by PESTPP-IES allowed for history matching and nonlinear uncertainty quantification. The model predictive uncertainties were coupled with climate uncertainties, including recharge uncertainty (derived from various global circulation models and emission scenarios) and sea-level rise uncertainty. Using PESTPP-OPT, the pumping rates in the freshwater lens were then maximized while avoiding the risk of well salinization and considering parameter, observation, and climate uncertainty. Results of the pumping optimization were compared with estimates of water demand uncertainty. This study used widely available, model-independent software and could be used to support groundwater management decision-making in other insular or coastal areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".