A hybrid approach to weather seasonality study and forecasting
Bibliographic record
Abstract
In the recent years, climate change produces a profound impact on the seasonality shifting. For example, even a slight variation in temperature may trigger an early spring start. This study presents a hybrid application of machine learning and deep learning to analyze the shifts in seasons across contiguous years and to accurately forecast weather variables. The daily meteorological features (minimum temperature, maximum temperature, wind speed, precipitation, snow cover, etc.) collected for the City of Toronto (Canada) area over a 20-year period spanning from 1st January 2001 till 31st December 2020 are used in the study. The temporal data has been pre-processed to normalize and rescale into a [0,1] range. The temporal data points are clustered to form distinct seasons applying various clustering algorithms: K-Means, Agglomerative hierarchical, Mean-shift, Affinity Propagation and Gaussian Mixture. The performance of these algorithms is compared to determine the most accurate seasonal clusters. The results show that the K-Means and Agglomerative hierarchical with ward linkage perform significantly better than other clustering algorithms to group temporal weather data. Obtained clusters are then used to analyze the shifts in seasonality for the period of 20 years. Furthermore, cascaded multi-layered Long Short-Term Memory (LSTM) deep learning model is used to forecast weather variables. Different variants of LSTM (i.e., single layer, multi-layer, and bidirectional) are evaluated to produce computationally inexpensive, lightweight, and accurate 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".