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• W4313362493 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." @default.
• W4313362493 created "2023-01-06" @default.
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• W4313362493 date "2022-12-28" @default.
• W4313362493 modified "2023-10-01" @default.
• W4313362493 title "A Machine Learning Approach for Air-Quality Forecast by Integrating GNSS Radio Occultation Observation and Weather Modeling" @default.
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• W4313362493 doi "https://doi.org/10.3390/atmos14010058" @default.
• W4313362493 hasPublicationYear "2022" @default.
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