Influence of land-sea breeze on long-term PM2.5 prediction in central and southern Taiwan using composite neural network
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Date
2022-09-08
Journal Title
Journal ISSN
Volume Title
Publisher
Research Square
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 overall superiority of
the proposed architecture for the southern and central Taiwan stations in the months when land-sea breeze events frequently
occur
Description
Main article
Keywords
Land-sea breeze, Long-term PM 2.5, Meteorological features, STRI component, Base component, RTP model