Bias correction of ANN based statistically ownscaled precipitation data for the Chaliyar river basin
Bibliographic record
Abstract
Any study to assess the impact of climate change on hydrology requires future climate scenarios at river basin scale. General Circulation Models (GCM) are the only reliable source for future climate scenarios, but they perform well only at coarse scale. Also, it may not be possible to straight away use the output from GCMs in hydrologic models applied at river basin scale. GCM simulations need to be downscaled to river basin scale. Uncorrected bias in the downscaled data, if any, should be corrected before the downscaled data is used in hydrologic applications. In this study, an advanced nonlinear bias correction method is applied to Artificial Neural Network (ANN) based downscaling models to obtain projections of monthly precipitation of station scale. The models were validated through application to downscale the monthly precipitation at two rain gauge stations, one in the Chaliyar river basin located in the humid tropics in Kerala, India, and other located close to it. The probable predictor variables are extracted from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data and simulations from the third generation Canadian Coupled Global Climate Model (CGCM3) for the twentieth century experiment, 20C3M. The potential predictors were selected based on the values of the correlation coefficient between NCEP predictors and predictand precipitation and also between NCEP predictors and GCM predictors. Separate models were developed for each station and for each of the season and separate sets of potential predictors were used in each of the models. The models were validated using the data after year 2000; the performance of the models was reasonably good except for a few extremes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".