DOWNSCALING GLOBAL CLIMATE MODELS USING MODULAR MODELS AND FUZZY COMMITTEES
Bibliographic record
Abstract
This study explores the use of modular models (MM) and fuzzy committee models (FCM) in downscaling rainfall data. The study compares the results with multilayer perceptron artificial neural network (MLP-ANN), time delay feed forward neural network (TLFN) and the statistical downscaling model (SDSM). Some statistical downscaling models of global climate data show clear seasonal effects. Recent works in MM have shown the usefulness of creating models based on regimes for seasonal based problems. The MLP-ANN models are setup applying exhaustive optimization of the structure using 10-fold cross validation. Then the MM based on regime identification using k-means clustering are built. This MM works with a pre-processing switching classifier that selects the best model for the actual input state. At the second level of integration, MM is complemented by a FCM that weights outputs. This approach is compared to the statistical downscaling model used as standard by NCEP (National centre for environmental prediction, Canada). These models are applied using NCEP re-analysis data. The problem posed is the downscaling of NCEP data into gauge precipitation for the Beles basin (Ethiopia). The results show that the MM and fuzzy committee model perform better than the models trained on full data sets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".