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dc.contributor.authorLiu, Tianyu
dc.contributor.authorYing, Yongzhi
dc.contributor.authorXu, Yanyan
dc.contributor.authorKe, Dengfeng
dc.contributor.authorSu, Kaile
dc.date.accessioned2019-05-29T12:58:23Z
dc.date.available2019-05-29T12:58:23Z
dc.date.issued2018
dc.identifier.isbn978-1-5090-6014-6
dc.identifier.issn2161-4407
dc.identifier.doi10.1109/ijcnn.2018.8489607
dc.identifier.urihttp://hdl.handle.net/10072/384045
dc.description.abstractWe present a new two-stage fine-grained air Particulate Matter (PM) prediction system using a variety of deep memory networks. Our model is significantly simpler than traditional weather report systems, which rely heavily on the atmospheric reaction equations and pollutant emissions inventories. Pollutant inventories are notoriously difficult to obtain and are often packed with declination for multiple reasons, while reaction equations are impossible to exhaust. These traditional models also tend to perform poorly when affected by strong convective weather. In contrast, our model does not need precise hand collected inventories of pollution sources. It can utilize the potential of Deep-Neural-Networks (DNN) to reveal the relationship among different locations and even find relations between known social events and air quality. Both potentially provide a valid path to air pollution control. The key to our approach is a well-tuned sophisticated attention based network that uses multiple GPUs, allowing us to transform a traditional sparse prediction problem into a sequence-to-sequence learning problem and train it end-to-end. By evaluating the model on over 1,625 instances of data of Northern China collected by our team, we show that it is not only computationally efficient but also accurately feasible compared with other optional models.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename2018 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofconferencetitle2018 International Joint Conference on Neural Networks (IJCNN)
dc.relation.ispartofdatefrom2018-07-08
dc.relation.ispartofdateto2018-07-13
dc.relation.ispartoflocationRio de Janeiro, Brazil
dc.relation.ispartofpagefrom1
dc.relation.ispartofpageto6
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleFine-Grained Air Quality Prediction using Attention Based Neural Network
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.hasfulltextNo Full Text
gro.griffith.authorSu, Kaile


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