Random Forest

Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest

This study develops and evaluates a machine learning-based forecasting system for ozone and PM2.5 at multiple AQS sites in the Pacific Northwest, demonstrating improved accuracy and reliability over existing CTM-based forecasts.

Feb 24, 2023

Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA

This study developed machine learning models to improve O3 forecasts for Kennewick, WA, demonstrating improved accuracy and reduced computational resources compared to traditional chemical transport models.

Feb 10, 2022