Kibirige, George WilliamHuang, Chiao-ChengLiu, Chao-LinChen, Meng-Chang2023-03-272023-03-272022-09-08http://www.suaire.sua.ac.tz/handle/123456789/5104Main articlePM 2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground- level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the overall superiority of the proposed architecture for the southern and central Taiwan stations in the months when land-sea breeze events frequently occurenLand-sea breezeLong-term PM 2.5Meteorological featuresSTRI componentBase componentRTP modelArticle