Adaptive learning algorithms for CNN models incorporating meteorological data for precise environmental predictions
Bibliographic record
Abstract
Abstract Weather forecasting through neural networks has increased and shown the potential for greater accuracy over recent years. Among numerous techniques, machine learning models provide more precise weather and climate prediction outcomes. The objective of this research was to analyze the highest and lowest monthly temperatures, as well as the highest wind speeds, in selected Nigerian cities, including Abuja, Lagos, Sokoto, Maiduguri, Calabar, and Port Harcourt through the use of cutting-edge machine learning technology such as deep learning (DL), and Convolution Neural Network (CNN). Our research approach involved compiling data on maximum and minimum temperatures and wind speeds from specific cities in Nigeria every month from 2000 to 2023. By successfully utilizing AMI, we pinpointed the optimal variables necessary for precisely evaluating the six cities as we built our model. The CNN algorithm stood out as a top-tier model in the test results due to its precise estimation of city temperature and wind speed values, highlighting exceptional generalization ability and minimal variance compared to the DL model.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".