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A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine

Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and doe...

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Detalles Bibliográficos
Autores principales: Gai, Rongli, Guo, Zhibin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905719/
https://www.ncbi.nlm.nih.gov/pubmed/36760628
http://dx.doi.org/10.3389/fpls.2023.1099668
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author Gai, Rongli
Guo, Zhibin
author_facet Gai, Rongli
Guo, Zhibin
author_sort Gai, Rongli
collection PubMed
description Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and does not take into account the weighting of the indicators. Therefore, this paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) andparticle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. Firstly, the combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP)AHP and Criteria Importance Though Intercrieria Correlation (CRITIC)CRITIC, and then the correlation between water quality indicators was calculated for feature selection. Secondly, the PSO-MSVM model was established using the water quality indicators obtained by ACGRA as input parameters for water environment quality assessment. The river water environment assessment methods of ACGRA and PSO-MSVM were applied to the evaluation of water environment quality in different watersheds in the country. Accuracy, precision, recall and root mean square errorRMSE were also introduced as model evaluation criteria. The results show that the river water environment assessment methods based on ACGRA and PSO-MSVM can evaluate the water environment quality more accurately.
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spelling pubmed-99057192023-02-08 A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine Gai, Rongli Guo, Zhibin Front Plant Sci Plant Science Most of the water quality indicators that affect the results of river water quality assessment are gray and localized, thus the correlation between water quality indicators can be calculated using gray correlation analysis (GRA).However, GRA takes equal weighting for water quality indicators and does not take into account the weighting of the indicators. Therefore, this paper proposes a river water quality assessment method based on improved grey correlation analysis (ACGRA) andparticle swarm optimization multi-classification support vector machine (PSO-MSVM) for assessing river water environment quality. Firstly, the combination weights of water quality indicators were calculated using Analytic Hierarchy Process (AHP)AHP and Criteria Importance Though Intercrieria Correlation (CRITIC)CRITIC, and then the correlation between water quality indicators was calculated for feature selection. Secondly, the PSO-MSVM model was established using the water quality indicators obtained by ACGRA as input parameters for water environment quality assessment. The river water environment assessment methods of ACGRA and PSO-MSVM were applied to the evaluation of water environment quality in different watersheds in the country. Accuracy, precision, recall and root mean square errorRMSE were also introduced as model evaluation criteria. The results show that the river water environment assessment methods based on ACGRA and PSO-MSVM can evaluate the water environment quality more accurately. Frontiers Media S.A. 2023-01-25 /pmc/articles/PMC9905719/ /pubmed/36760628 http://dx.doi.org/10.3389/fpls.2023.1099668 Text en Copyright © 2023 Gai and Guo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Gai, Rongli
Guo, Zhibin
A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title_full A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title_fullStr A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title_full_unstemmed A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title_short A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
title_sort water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905719/
https://www.ncbi.nlm.nih.gov/pubmed/36760628
http://dx.doi.org/10.3389/fpls.2023.1099668
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