Advanced air quality forecasting with ensemble ML (Machine Learning) models,
NASA satellite data fusion, and real-time health insights
Our comprehensive data pipeline integrates multiple NASA and EPA data sources through advanced machine learning models to deliver real-time air quality intelligence.
EPA AirNow + OpenAQ primary data → NASA TEMPO spatial context →ML Ensemble (XGBoost + TensorFlow) →Real-time API with 5-minute caching
Our ensemble ML models continuously improve through automated retraining, achieving industry-leading accuracy in air quality forecasting.
Achieves 0.94 MAE (Mean Absolute Error) with continuous improvement from 5.0 → 0.8 AQI (Air Quality Index) units over time. Auto-retrains every 24 hours with ≥20 validated forecast points.
Our system implements a sophisticated 24-hour retraining cycle, continuously validating forecasts and improving model accuracy.
Forecasts validated against real observations → 94% category accuracy → Auto-retrain with ≥20 validated points → Continuous improvement cycle
We seamlessly integrate data from multiple sources with different temporal and spatial resolutions to create a comprehensive view of air quality conditions.
NASA TEMPO (2.1km satellite) + EPA AirNow + OpenAQ (ground truth) +MERRA-2 (atmospheric mixing) + Open-Meteo (weather patterns)
Our models demonstrate consistent improvement over time, with accuracy gains driven by increasing training data and advanced feature optimization.
Training data: 10K → 70K+ points | Model complexity: 1K → 4.5K+ parameters | Continuous optimization over 12 months
Our production system maintains exceptional performance with sub-200ms response times, 80% cache hit rates, and enterprise-grade reliability.
150ms avg response time | 80% cache hit rate |50+ requests/second | 5-minute intelligent caching
Enterprise-grade infrastructure powering real-time air quality intelligence
Experience advanced air quality forecasting with NASA satellite data, ensemble ML models, and real-time health insights
Launch Platform