Back to Search
Start Over
A Machine Learning Approach for Air-Quality Forecast by Integrating GNSS Radio Occultation Observation and Weather Modeling.
- Source :
- Atmosphere; Jan2023, Vol. 14 Issue 1, p58, 15p
- Publication Year :
- 2023
-
Abstract
- Air-quality monitoring and forecasting are crucial for atmosphere pollution control and management. We propose an innovative data-driven framework for air quality index (AQI) prediction by integrating GNSS radio occultation (GNSS-RO) observation and weather modeling. Empowered by the state-of-the-art machine learning approach, our method can effectively predict regional AQI with a comparable accuracy much more quickly than the traditional numerical modeling and simulation approach. In a real case study using a representative region of China, our data-driven approach achieves a 2000 times speedup; meanwhile, the prediction error measured by rRMSE is only 2.4%. We investigate further the effects of different models, hyperparameters, and meteorological factors on the performance of our AQI prediction framework, and reveal that wind field and atmospheric boundary-layer height are important influencing factors of AQI. This paper showcases a direct application of GNSS-RO observation in assisting in forecasting regional AQI. From a machine learning point of view, it provides a new way to leverage the unique merits of GNSS atmospheric remote sensing technology with the help of the more traditional weather forecasting modeling approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20734433
- Volume :
- 14
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Atmosphere
- Publication Type :
- Academic Journal
- Accession number :
- 161435930
- Full Text :
- https://doi.org/10.3390/atmos14010058