Integration of an evolutionary algorithm into the ensemble Kalman filter and the particle filter for hydrologic data assimilation
Bibliographic record
Abstract
Data assimilation (DA) methods continue to evolve in the design of streamflow forecasting procedures. Critical components for efficient DA include accurate description of states, improved model parameterizations, and estimation of the measurement error. Information about these components are usually assumed or rarely incorporated into streamflow forecasting procedures. Knowledge of these components could be gained through the generation of a Pareto-optimal set – a set of competitive members that are not dominated by other members when compared using evaluation objectives. This study integrates Pareto-optimality into the ensemble Kalman filter (EnKF) and the particle filter (PF). Comparisons are made between three methods: evolutionary data assimilation (EDA) and methods based on the integration of Pareto-optimality into the EnKF (ParetoEnKF) and into the PF (ParetoPF). The methods are applied to assimilate daily streamflow into the Sacramento Soil Moisture Accounting model in the Spencer Creek watershed in Canada. The updated members are applied to forecast streamflows for up to 10 days ahead, where forecasts for 1 day, 5 day and 10 day lead times are compared to observations. The results show that updated estimates are similar for all three methods. An evaluation of updated members for multi-step forecasting revealed that EDA had the highest forecast accuracy compared to ParetoEnKF and ParetoPF, which have similar accuracies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".