Influence of land‐sea breeze On pm 2.5 prediction in central and Southern Taiwan using composite neural network

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dc.creatorKibirige, George William
dc.creatorHuang, Chiao Cheng
dc.creatorLin Liu, Chao
dc.creatorChang Chen, Meng
dc.date.accessioned2023-03-16T06:22:44Z
dc.date.accessioned2025-08-05T07:22:55Z
dc.date.available2023-03-16T06:22:44Z
dc.date.created2023-03-16T06:22:44Z
dc.date.issued2023
dc.description.abstractPM 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 .
dc.identifierhttp://www.suaire.sua.ac.tz/handle/123456789/5021
dc.identifier.urihttp://repository.costech.or.tz/handle/20.500.14732/97342
dc.languageen
dc.publisherNature portfolio
dc.subjectTaiwan
dc.subjectpm 2.5
dc.subjectLand‐sea breeze
dc.subjectSea breeze
dc.subjectNeural network
dc.titleInfluence of land‐sea breeze On pm 2.5 prediction in central and Southern Taiwan using composite neural network
dc.typeArticle

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