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

Air 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 m...

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Main Authors: Zaini N., Ean L.W., Ahmed A.N.
Other Authors: 56905328500
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-26461
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spelling my.uniten.dspace-264612023-05-29T17:10:46Z Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach Zaini N. Ean L.W. Ahmed A.N. 56905328500 55324334700 57214837520 Air 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 memory Carbon 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. Final 2023-05-29T09:10:46Z 2023-05-29T09:10:46Z 2021 Conference Paper 10.1007/978-3-030-90235-3_45 2-s2.0-85120525365 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120525365&doi=10.1007%2f978-3-030-90235-3_45&partnerID=40&md5=b98f074b1af122bf4ce52379d6a222b4 https://irepository.uniten.edu.my/handle/123456789/26461 13051 LNCS 518 529 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Air 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 memory
author2 56905328500
author_facet 56905328500
Zaini N.
Ean L.W.
Ahmed A.N.
format Conference Paper
author Zaini N.
Ean L.W.
Ahmed A.N.
spellingShingle Zaini N.
Ean L.W.
Ahmed A.N.
Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
author_sort Zaini N.
title Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
title_short Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
title_full Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
title_fullStr Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
title_full_unstemmed Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
title_sort forecasting of carbon monoxide concentration based on sequence-to-sequence deep learning approach
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806425754804682752
score 13.188404

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