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Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach

Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses...

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Main Authors:	Rezaie-Balf, Mohammad, Attar, Nasrin Fathollahzadeh, Mohammadzadeh, Ardashir, Murti, Muhammad Ary, Ahmed, Ali Najah, Fai, Chow Ming, Nabipour, Narjes, Alaghmand, Sina, El-Shafie, Ahmed
Format:	Article
Published:	Elsevier 2020
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/25266/ https://doi.org/10.1016/j.jclepro.2020.122576
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id	my.um.eprints.25266
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spelling	my.um.eprints.252662020-08-05T07:33:17Z http://eprints.um.edu.my/25266/ Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach Rezaie-Balf, Mohammad Attar, Nasrin Fathollahzadeh Mohammadzadeh, Ardashir Murti, Muhammad Ary Ahmed, Ali Najah Fai, Chow Ming Nabipour, Narjes Alaghmand, Sina El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is conducted on existing solutions through data-driven models. In addition, the ensemble Kalman filter is found to be a suitable data assimilation method, which is successfully applied in hydrological variables modeling and other complexes, nonlinear, and chaotic problems. In this study, a new application of ensemble Kalman filter-artificial neural network is proposed to predict water quality index using physicochemical parameters for two commonly pollutant rivers, namely Klang and Langat, in Malaysia. As a further attempt, in order to improve the models’ performance, a new preprocessing technique is adopted as the newly constructed assimilated model. The results confirm that ensemble hybrid based intrinsic time-scale decomposition has reduced root mean square error by 24% for Klang and 34% for Langat, respectively, compared with the intrinsic time-scale decomposition-conventional neural network model. Overall, the developed assimilated methodology shows the robustness of the proposed ensemble hybrid model in analyzing water quality index over monthly horizons that experts could evaluate the water quality of rivers more efficiently. © 2020 Elsevier Ltd Elsevier 2020 Article PeerReviewed Rezaie-Balf, Mohammad and Attar, Nasrin Fathollahzadeh and Mohammadzadeh, Ardashir and Murti, Muhammad Ary and Ahmed, Ali Najah and Fai, Chow Ming and Nabipour, Narjes and Alaghmand, Sina and El-Shafie, Ahmed (2020) Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach. Journal of Cleaner Production, 271. p. 122576. ISSN 0959-6526 https://doi.org/10.1016/j.jclepro.2020.122576 doi:10.1016/j.jclepro.2020.122576
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Rezaie-Balf, Mohammad Attar, Nasrin Fathollahzadeh Mohammadzadeh, Ardashir Murti, Muhammad Ary Ahmed, Ali Najah Fai, Chow Ming Nabipour, Narjes Alaghmand, Sina El-Shafie, Ahmed Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
description	Water quality has a crucial impact on human health; therefore, water quality index modeling is one of the challenging issues in the water sector. The accurate prediction of water quality index is an essential requisite for water quality management, human health, public consumption, and domestic uses. A comprehensive review as an initial attempt is conducted on existing solutions through data-driven models. In addition, the ensemble Kalman filter is found to be a suitable data assimilation method, which is successfully applied in hydrological variables modeling and other complexes, nonlinear, and chaotic problems. In this study, a new application of ensemble Kalman filter-artificial neural network is proposed to predict water quality index using physicochemical parameters for two commonly pollutant rivers, namely Klang and Langat, in Malaysia. As a further attempt, in order to improve the models’ performance, a new preprocessing technique is adopted as the newly constructed assimilated model. The results confirm that ensemble hybrid based intrinsic time-scale decomposition has reduced root mean square error by 24% for Klang and 34% for Langat, respectively, compared with the intrinsic time-scale decomposition-conventional neural network model. Overall, the developed assimilated methodology shows the robustness of the proposed ensemble hybrid model in analyzing water quality index over monthly horizons that experts could evaluate the water quality of rivers more efficiently. © 2020 Elsevier Ltd
format	Article
author	Rezaie-Balf, Mohammad Attar, Nasrin Fathollahzadeh Mohammadzadeh, Ardashir Murti, Muhammad Ary Ahmed, Ali Najah Fai, Chow Ming Nabipour, Narjes Alaghmand, Sina El-Shafie, Ahmed
author_facet	Rezaie-Balf, Mohammad Attar, Nasrin Fathollahzadeh Mohammadzadeh, Ardashir Murti, Muhammad Ary Ahmed, Ali Najah Fai, Chow Ming Nabipour, Narjes Alaghmand, Sina El-Shafie, Ahmed
author_sort	Rezaie-Balf, Mohammad
title	Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
title_short	Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
title_full	Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
title_fullStr	Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
title_full_unstemmed	Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach
title_sort	physicochemical parameters data assimilation for efficient improvement of water quality index prediction: comparative assessment of a noise suppression hybridization approach
publisher	Elsevier
publishDate	2020
url	http://eprints.um.edu.my/25266/ https://doi.org/10.1016/j.jclepro.2020.122576
_version_	1680857014870212608
score	13.211869

Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression hybridization approach

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