Accurate monthly forecasting of Rainfall pattern in Atlantic climates: an Empirical Fourier Decomposition-based Deep ensemble learning paradigm
Bibliographic record
Abstract
The rainfall pattern plays a vital role in agriculture and overall climate resilience in the Atlantic provinces of Canada. Prince Edward Island and New Brunswick, two Atlantic provinces, have great potential to grow potatoes, grains, and blueberries. Thus, accurate forecasting of the rainfall pattern helps farmers determine the optimal time for planting, irrigation, fertilisation, and harvesting based on predicted rainfall patterns. Here, a new complementary multi-level intelligent framework comprised of the recursive feature elimination (RFE), Empirical Fourier Decomposition (EFD), and deep ensemble random vector functional link (Deep RVFL) has been developed to forecast the monthly (one month ahead) rainfall pattern in Charlottetown and Fredericton stations. Aiming for this, first, the significant antecedent information (lag sequences) was indicated using the RFE scheme. Then, all the optimal lags were decomposed using the EDF scheme to deduce the complexities of rainfall sub-component signals before feeding the Deep RVFL algorithm. Two comparative deep learning models, namely, RVFL and CNN-LSTM, were incorporated with the implemented multi-level pre-processing scheme in hybrid and standalone forms. In order to validate the models, several statistical indices, such as correlation coefficient (R), root mean square error (RMSE), and Kling-Gupta efficiency (KGE), scatter plots, signal trend analysis, and diagnostic assessment, were utilized. The outcomes of the results ascertained that the RFE-EDF-Deep RVFL framework, owing to superior forecasting performance, outperformed the RFE-EDF-Deep CNN-LSTM, RFE-EDF-RVFL and all the standalone counterpart models.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".