Cargando…
Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest
Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecast...
Autores principales: | Fan, Kai, Dhammapala, Ranil, Harrington, Kyle, Lamb, Brian, Lee, Yunha |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999009/ https://www.ncbi.nlm.nih.gov/pubmed/36910164 http://dx.doi.org/10.3389/fdata.2023.1124148 |
Ejemplares similares
-
Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA
por: Fan, Kai, et al.
Publicado: (2022) -
Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data
por: Saminathan, S., et al.
Publicado: (2023) -
DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
por: Brust, Colin, et al.
Publicado: (2021) -
Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River
por: Xiao, Lu, et al.
Publicado: (2022) -
Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana
por: Carter, S., et al.
Publicado: (2021)