A Hybrid Perturbation and Morris Approach for Identifying Sensitive Parameters in Surface Water Quality Models
Bibliographic record
Abstract
Surface water quality models (SWQM) are always developed as universal frameworks so that they can be flexibly employed to simulate a large variety of water bodies. These models are often over-parameterized (more parameters than needed are included in these models). As a result, it is necessary to identify sensitive parameters when these models are applied to the simulations of specific water bodies. Sensitivity analysis has been widely used as an effective tool to undertake the task. In this study, a hybrid approach was developed through integrating the parameter perturbation method and the Morris method into a general SWQM-parameter sensitivity analysis framework. The approach was applied to Lake Maumelle in Arkansas with its hydrodynamics and water quality being simulated by the model CE-QUAL-W2. The sensitivities of the 96 model parameters were firstly evaluated by the parameter perturbation method in the simulation of the variables including temperature, ammonium, nitrate-nitrite, dissolved oxygen, total phosphorus and chlorophyll a, and 51 of them were found sensitive. The sensitivities of the 51 parameters were further investigated using the Morris method. It was found that each output variable was strongly sensitive to a distinctive set of parameters. It is also observed that the highly sensitive parameters display nonlinear relationships with the model outputs or strong correlations with other parameters. The obtained results from this study could provide a scientific base and solid start for the calibration, validation and application of the model.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".