Multi-objective calibration of hydrological models and data assimilation using genetic algorithms
Bibliographic record
Abstract
This dissertation investigates parameter estimation and data assimilation in the context of hydrological modeling to improve water resource decisions. Using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the Soil and Water Assessment Tool was calibrated in a multi-objective fashion for simulations of streamflow. The resulting output is a Pareto frontier comprising a set of incomparable solutions which form a trade-off between two model evaluation objectives. Using the Pareto frontier, the study has developed an automated framework to select solutions from the trade-off surface by evaluating the distribution of solutions in objective space and parameter space. The framework selects solutions with four unique properties including a representative pathway in parameter space, a basin of attraction in objective space, a proximity to the origin in objective space, and a balanced compromise between objective space and parameter space (denoted BCOP). Evaluation of the four auto-selection methods for 15 calibration outputs which are each evaluated across 15 different validation periods show that BCOP perform consistently better than other methods. Additionally, a model characterization framework (MCF) was developed and it uses cluster analysis to examine the distribution of solutions, and conditional probability to combine linkages between the distributions of solutions in both spaces. The MCF computes two indicators: robustness and choice index - which categorizes incomparable sets of solutions to select parameter set(s) with desired properties/behaviour. The evaluation of linkages between robustness and choice index for 225 separate evaluations show that robustness is critical to the performance of solutions across several validation periods. Furthermore, the study has improved a time series of soil moisture through a joint assimilation of satellite brightness temperature and soil moisture. The NSGA-II was applied in a data assimilation framework to merge two soil moisture estimates. One soil moisture was estimated from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) by assimilating brightness temperature into a radiative transfer model. The other estimate of soil moisture was generated from the Canadian Land Surface Scheme (CLASS). A comparison between the assimilated soil moisture and 'in situ' dataset showed an improvement in accuracy and temporal pattern that was accomplished through the assimilation framework.
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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 | 0.004 | 0.008 |
| 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 | 0.001 | 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".