A Framework for Short-Term Forecasting of Extreme Weather Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it