Statistical Downscaling of Rainfall Under Climate Change in Krishna River Sub-basin of Andhra Pradesh, India Using Artificial Neural Network (ANN)
Bibliographic record
Abstract
Due to the very coarse spatial resolution of the different global circulation model (GCM), we cannot use them in their natural form to study the various impacts of climate change.For matching this spatial inequality between the GCMs output (predictor) and historical precipitation data (predictands), we need to establish a relation between them which is known as downscaling.In the present study, we tried to examine the efficiency of the Artificial Neural Network (ANN) with Principal Component Analysis (PCA) for downscaling the rainfall for 3 districts of Andhra Pradesh of India.Firstly, for all the regions, the downscaling was performed by using ANN.Then seasonal and annual analysis was performed based on the R 2 and RMSE.The results show that the ANN worked adequately based on the statistical parameters.The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, re-analysis from the National Centre for Environmental Prediction (NCEP) as GCM model, and observed rainfall data as the observed rainfall.The analysis was performed for the three RCPs scenario, RCP 2.6, 4.5 and 8.5.Finally, the ANN model is applied to downscale the precipitation.
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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) | 0.000 | 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".