Influence of land‐sea breeze On pm 2.5 prediction in central and Southern Taiwan using composite neural network
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Date
2023
Journal Title
Journal ISSN
Volume Title
Publisher
Nature portfolio
Abstract
PM 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 superiority of the
proposed architecture for stations where land-sea breezes frequently occur in the southern and central
Taiwan in the months when land-sea breeze dominates the accumulation of PM 2.5 .
Description
Scientific report
Keywords
Taiwan, pm 2.5, Land‐sea breeze, Sea breeze, Neural network