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

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

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. For example, a CTM-based operational forecasting system for air quality over the...

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Bibliographic Details
Main Authors: Kai Fan, Ranil Dhammapala, Kyle Harrington, Ryan Lamastro, Brian Lamb, Yunha Lee
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-02-01
Series:Frontiers in Big Data
Subjects:
machine learning
air quality forecasts
ozone
random forest
multiple linear regression
Online Access:https://www.frontiersin.org/articles/10.3389/fdata.2022.781309/full

Internet

https://www.frontiersin.org/articles/10.3389/fdata.2022.781309/full

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