Publication:
Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach

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dc.citedby1
dc.contributor.authorZaini N.en_US
dc.contributor.authorEan L.W.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorid56905328500en_US
dc.contributor.authorid55324334700en_US
dc.contributor.authorid57214837520en_US
dc.date.accessioned2023-05-29T09:10:46Z
dc.date.available2023-05-29T09:10:46Z
dc.date.issued2021
dc.descriptionAir quality; Brain; Carbon monoxide; Errors; Forecasting; Mean square error; Network architecture; Air pollutants; Carbon monoxide concentration; Convolutional neural network; Deep learning; Forecasting models; Human health; Learning approach; Monitoring stations; Root mean square errors; Sequence models; Long short-term memoryen_US
dc.description.abstractCarbon monoxide (CO) is one of the dangerous air pollutants due to its negative impact on human health. Therefore, accurate forecasting of CO concentration is essential to control air pollution. This study aims to forecast the concentration of CO using sequences to sequence models namely convolutional neural network and long short-term memory (CNN-LSTM) and sequence to sequence LSTM (seq2seq LSTM). The proposed forecasting models are validated using hourly air quality datasets from six monitoring stations in Selangor to forecast CO concentration at 1�h to 6�h ahead of the time horizon. The performances of proposed models are evaluated in terms of statistical equations namely root mean square error (RMSE), mean square error (MAE) and mean percentage error (MAPE). CNN-LSTM and seq2seq LSTM model excellently forecast air pollutant concentration for 6�h ahead with RMSE of 0.2899 and 0.2215, respectively. Additionally, it is found that seq2seq LSTM has slightly improved CNN-LSTM indicates the effectiveness of the architecture in the forecasting. However, both proposed architectures illustrate promising results and are reliable in the forecasting of CO concentration. � 2021, Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-90235-3_45
dc.identifier.epage529
dc.identifier.scopus2-s2.0-85120525365
dc.identifier.spage518
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120525365&doi=10.1007%2f978-3-030-90235-3_45&partnerID=40&md5=b98f074b1af122bf4ce52379d6a222b4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26461
dc.identifier.volume13051 LNCS
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.titleForecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approachen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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