AstroDreamers is developing advanced machine learning capabilities to forecast PM2.5 levels 6-24 hours in advance. Using historical data from OpenAQ and NASA TEMPO observations, we're training two prediction models:

LightGBM Model: A gradient boosting framework optimized for speed and accuracy with time-series data. The model processes historical PM2.5 measurements, meteorological factors (temperature, humidity, wind speed), and temporal patterns (hour of day, day of week, seasonality).

Random Forest Model: An ensemble learning method that creates multiple decision trees to predict future pollution levels. This model excels at handling non-linear relationships and identifying which factors most strongly influence PM2.5 concentrations.

How the prediction works:

1. Data Collection: Aggregate 6-12 months of historical PM2.5 data from OpenAQ for each location

2. Feature Engineering: Extract time-based features (hour, day, month), calculate rolling averages, and incorporate weather data

3. Model Training: Train both LightGBM and Random Forest models on 80% of data, validate on remaining 20%

4. Ensemble Prediction: Combine predictions from both models using weighted averaging for improved accuracy

5. Real-time Forecasting: Generate 6-hour and 24-hour forecasts updated every hour

Status: Currently in development, targeted for Phase 2 release. Initial models trained on data from 10 major cities with plans to expand coverage globally.