Predicting Flight Trajectory in Convective Weather through Boosted Spatiotemporal Deep Learning Ensemble
Bibliographic record
Abstract
Flight trajectory prediction is one of the key issues in ensuring the safety of air traffic, providing the air traffic controller with the foresight of flight conflicts so that control instructions for pilots can be preconceived. In a complicated mechanism, flight trajectories can be severely affected by convective weather, making accurately predicting trajectories challenging. To address this problem, we propose a boosted spatiotemporal deep learning ensemble for mining the law of how convective weather affects flight trajectory stretching. Instead of conventionally representing trajectory data in a geographic coordinate system, we design a relative coordinate system for gaining new trajectory features which tangibly reflect trajectory’s relations with planned route and convective weather. Besides, we raise a boosted ensemble framework of spatiotemporal deep learning models, trained by the samples pairing sequential trajectory with graphical weather, dedicating to strengthen the mining of the high‐value training samples that involve explicit flight deviations caused by convective weather. The experiments using actual flight and weather data demonstrate our method’s superiority in predicting flight trajectory affected by convective weather.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".