A Framework for Short-Term Forecasting of Extreme Weather Events
Bibliographic record
Abstract
This paper proposes a machine learning model to predict the accuracy of extreme rainfall events by exploiting the concept of quality control chart in operations management. In this framework, we introduce the 3σ framework, a novel approach to short-term rainfall forecasting by presenting the rainfall process in a statistical quality control perspective. The framework consists of two parts: (1) a 3σ chart and (2) a machine learning classification model. Rainfall intensity is categorized into three classes based on the 3σ chart. The model is able to effectively capture sequential rainfall trends and predict precipitation classes up to 24 hours in advance. The results indicate that the framework achieves high performance in key metrics, including loss, precision, and recall, with consistent alignment between the training and validation phases. Furthermore, the comparison between predicted and actual rainfall classes confirms the model’s effectiveness in detecting both the occurrence and magnitude of severe rainfall events, although slight overestimations were observed in isolated cases. In general, the framework has significant potential for integration into real-time early warning systems, helping to reduce the impact of climate-driven extreme weather events by allowing faster and more interpretable alerts for floods, landslides, and related hazards.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".