Hydrologic model Calibration using Fuzzy TSK surrogate model
Bibliographic record
Abstract
In order to find the best parameter set of a hydrologic model, error minimization is often used. In this case, optimization is performed using fuzzy TSK surrogate model, a fuzzy Tagaki Sugeno Kang based method chosen for its efficiency and robustness. Parameter space exploration is performed using the surrogate model, thus avoiding the use of the computationally expensive full model. The dimensionality of the problem is not very high (requires the calibration of 15-150 parameters), however the computational cost for the evaluation of the cost function is significant. In order to evaluate the cost function for a single set of parameters, 2hrs (for watersheds smaller than 100 km/sup 2/) to 24 hrs (for watersheds larger than 100,000 km/sup 2/) of computer time is required. To avoid this cost, the surrogate model is constructed to approximate the actual model, which maps the known data points. Since the surrogate model is inexpensive to evaluate, we can explore the model space and find the optimum value cheaply. In each iteration, the surrogate model is used to predict the minimizer of the actual model, then the actual model is evaluated at the predicted minimizer and the surrogate is updated to include the new data. This process continues until sufficient cost function (error) reduction is achieved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".