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dc.contributor.authorLiu, Hen_NZ
dc.contributor.authorLu, Gen_NZ
dc.contributor.authorWang, Yen_NZ
dc.contributor.authorKasabov, Nen_NZ
dc.date.accessioned2022-02-04T03:00:16Z
dc.date.available2022-02-04T03:00:16Z
dc.date.copyright2021en_NZ
dc.identifier.citationAerosol and Air Quality Research 21, 200247. https://doi.org/10.4209/aaqr.2020.05.0247
dc.identifier.issn1680-8584en_NZ
dc.identifier.issn2071-1409en_NZ
dc.identifier.urihttp://hdl.handle.net/10292/14879
dc.description.abstractIn recent years, the dangers that air pollutants pose to human health and the environment have received widespread attention. Although accurately predicting the air quality is essential to managing pollution and developing control policies, traditional forecasting models have not been able to simulate the seasonal and diurnal variation in air pollutant concentrations. Furthermore, inadequate processing of the available spatio-temporal data has precluded the capture of predictive historical patterns. Therefore, we have developed a staging evolving spiking neural network (eSNN) model named Staging-eSNN that first employs a time series clustering algorithm to distinguish the seasonal from the diurnal variation in the PM2.5 concentration. We then predict the concentrations in Beijing and Shanghai 1, 3, 6, 12 and 24 hours in advance. Various evaluation indicators show that the Staging-eSNN model achieves higher performance than the support vector regression (SVR), random forest (RF) and other eSNN models.en_NZ
dc.publisherTaiwan Association for Aerosol Research
dc.relation.urihttps://aaqr.org/articles/aaqr-20-05-oa-0247
dc.rightsThe Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
dc.subjectAir pollutant prediction; PM2.5 hourly concentration; Seasonality; Evolving spiking neural networks; Time series clustering
dc.titleEvolving Spiking Neural Network Model for PM2.5 Hourly Concentration Prediction Based on Seasonal Differences: A Case Study on Data from Beijing and Shanghaien_NZ
dc.typeJournal Article
dc.rights.accessrightsOpenAccessen_NZ
dc.identifier.doi10.4209/aaqr.2020.05.0247en_NZ
aut.relation.endpage15
aut.relation.issue2en_NZ
aut.relation.startpage1
aut.relation.volume21en_NZ
pubs.elements-id398097
aut.relation.journalAerosol and Air Quality Researchen_NZ


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