Enhancing Few-Shot Learning for Tropical Cyclone Severity Prediction: A Deep Learning Approach
Bibliographic record
Abstract
The accurate prediction of tropical cyclone severity is of paramount importance in mitigating the potential damages arising from such catastrophic events.Constant monitoring and precise forecasting of tropical cyclones using remote satellite imagery from the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) are crucial.However, the challenge encountered with the current deep learning approach to image classification is its reliance on extensive labelled data and its limitations in few-shot learning.This study proposes a novel few-shot learning (FSL) approach for the prediction of tropical cyclone severity.In conjunction with FSL, the earth mover's distance (EMD) metric is employed to compute the distance between dense regions, thereby determining the relevance of an image.The methodology harnesses a remote satellite dataset provided by MOSDAC.The proposed approach is underpinned by the human capacity to identify novel classes from a limited number of samples, leveraging previously acquired knowledge.The FSL methodology adopts a meta-learning mechanism, enabling enhanced understanding of the data and facilitating the generalization of a new class of data.The results indicate that the FSL+EMD-based models outperform other state-of-the-art models, achieving a prediction accuracy of 85.8% in forecasting tropical cyclone severity from remote satellite imagery.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".