Staff View: Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data serie...

Full description

Saved in:

Bibliographic Details
Main Authors:	Fu, Minglei, Le, Caowei, Fan, Tingchao, Prakapovich, Ryhor, Manko, Dmytro, Dmytrenko, Oleh, Lande, Dmytro, Shahid, Shamsuddin, Yaseen, Zaher Mundher
Format:	Article
Published:	Springer Science and Business Media Deutschland GmbH 2021
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/94129/ http://dx.doi.org/10.1007/s11356-021-15574-y
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.utm.94129
record_format	eprints
spelling	my.utm.941292022-02-28T13:32:36Z http://eprints.utm.my/id/eprint/94129/ Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction Fu, Minglei Le, Caowei Fan, Tingchao Prakapovich, Ryhor Manko, Dmytro Dmytrenko, Oleh Lande, Dmytro Shahid, Shamsuddin Yaseen, Zaher Mundher TA Engineering (General). Civil engineering (General) The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson’s correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson’s correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring. Springer Science and Business Media Deutschland GmbH 2021-12 Article PeerReviewed Fu, Minglei and Le, Caowei and Fan, Tingchao and Prakapovich, Ryhor and Manko, Dmytro and Dmytrenko, Oleh and Lande, Dmytro and Shahid, Shamsuddin and Yaseen, Zaher Mundher (2021) Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction. Environmental Science and Pollution Research, 28 (45). pp. 64818-64829. ISSN 0944-1344 http://dx.doi.org/10.1007/s11356-021-15574-y DOI:10.1007/s11356-021-15574-y
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Fu, Minglei Le, Caowei Fan, Tingchao Prakapovich, Ryhor Manko, Dmytro Dmytrenko, Oleh Lande, Dmytro Shahid, Shamsuddin Yaseen, Zaher Mundher Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
description	The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson’s correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson’s correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson’s correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
format	Article
author	Fu, Minglei Le, Caowei Fan, Tingchao Prakapovich, Ryhor Manko, Dmytro Dmytrenko, Oleh Lande, Dmytro Shahid, Shamsuddin Yaseen, Zaher Mundher
author_facet	Fu, Minglei Le, Caowei Fan, Tingchao Prakapovich, Ryhor Manko, Dmytro Dmytrenko, Oleh Lande, Dmytro Shahid, Shamsuddin Yaseen, Zaher Mundher
author_sort	Fu, Minglei
title	Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
title_short	Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
title_full	Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
title_fullStr	Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
title_full_unstemmed	Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
title_sort	integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2021
url	http://eprints.utm.my/id/eprint/94129/ http://dx.doi.org/10.1007/s11356-021-15574-y
_version_	1726791485517463552
score	13.211869

Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction

Similar Items