Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks
Bibliographic record
Abstract
Abstract In this paper, downscaling models are developed using a Linear Multiple Regression (LMR) and Artificial Neural Networks (ANNs) for obtaining projections of mean monthly maximum and minimum temperatures (Tmax and Tmin) to lake‐basin scale in an arid region in India. The effectiveness of these techniques is demonstrated through application to downscale the predictands for the Pichola lake region in Rajasthan State in India, which is considered to be a climatically sensitive region. The predictor variables are extracted from: (i) the National Centers for Environmental Prediction (NCEP) reanalysis dataset for the period 1948–2000; and (ii) the simulations from the third‐generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001–2100. The scatter‐plots and cross‐correlations are used for verifying the reliability of the simulation of the predictor variables by the CGCM3 and to study the predictor–predictand relationships. The performance of the linear multiple regression and ANN models was evaluated based on several statistical performance indicators. The ANN‐based models are found to be superior to LMR‐based models and subsequently, the ANN‐based model is applied to obtain future climate projections of the predictands. An increasing trend is observed for Tmax and Tmin for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario by using predictors. Copyright © 2011 Royal Meteorological Society
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".