[ THE PROBLEM ]
Air quality is shaped by more than yesterday's reading.
Pollution evolves with weather, atmospheric mixing, regional transport and emissions. VAYU-SENTINEL explores a forecasting system that combines air-quality observations with meteorological and regional signals to estimate hourly PM2.5 over future horizons.
[ RESEARCH DIRECTION ]
The research stack includes Python-based data processing, gradient-boosted baselines, graph-learning experiments, residual correction and calibrated uncertainty. Planned serving direction includes a FastAPI service and monitoring dashboard.
Historical V1 baseline results exist in project notes. The complete 72-horizon training run has not been completed. This is a research prototype, not an operational forecasting service.
[ WHAT WE ARE TESTING ]
- Weather coupling and regional transport context.
- Chronological, leakage-aware validation.
- Per-horizon comparison against meaningful baselines.
- Uncertainty intervals calibrated against observed outcomes.