Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China

In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Feifei Fan, Zhengran Qiao, Lifeng Wu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	chemical oxygen demand deformable grey multivariable convolution primary industry secondary industry tertiary industry zhanghe river Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/ae446d7b12a541c18949c7d826f21924
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ae446d7b12a541c18949c7d826f21924
record_format	dspace
spelling	oai:doaj.org-article:ae446d7b12a541c18949c7d826f219242021-11-06T11:16:50ZUsing a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China0273-12231996-973210.2166/wst.2021.267https://doaj.org/article/ae446d7b12a541c18949c7d826f219242021-08-01T00:00:00Zhttp://wst.iwaponline.com/content/84/3/777https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator). HIGHLIGHTS The proposed model has the highest prediction accuracy.; The water quality in Zhanghe is predicted considering the socio-economic development.; The proposed model can be used in other rivers.;Feifei FanZhengran QiaoLifeng WuIWA Publishingarticlechemical oxygen demanddeformable grey multivariable convolutionprimary industrysecondary industrytertiary industryzhanghe riverEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 3, Pp 777-792 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	chemical oxygen demand deformable grey multivariable convolution primary industry secondary industry tertiary industry zhanghe river Environmental technology. Sanitary engineering TD1-1066
spellingShingle	chemical oxygen demand deformable grey multivariable convolution primary industry secondary industry tertiary industry zhanghe river Environmental technology. Sanitary engineering TD1-1066 Feifei Fan Zhengran Qiao Lifeng Wu Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
description	In order to assess the social factors affecting the water quality of the Zhanghe River and predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality of the Zhanghe River in the next few years, a deformation derivative cumulative grey multiple convolution model (DGMC(1,N)) was applied. In order to improve the accuracy of the model, the accumulation of deformation derivatives is introduced, and the particle swarm optimization algorithm is used to solve the optimal order. The DGMC(1,N) model was compared with GM(1,2) and GM(1,1) models. The results show that the DGMC(1,N) model has the highest prediction accuracy. Finally, DGMC(1,N) model is used to predict the potential impact of growth in primary, secondary, tertiary industries and population on water quality in the Zhanghe River (using chemical oxygen demand (COD) as the water quality indicator). HIGHLIGHTS The proposed model has the highest prediction accuracy.; The water quality in Zhanghe is predicted considering the socio-economic development.; The proposed model can be used in other rivers.;
format	article
author	Feifei Fan Zhengran Qiao Lifeng Wu
author_facet	Feifei Fan Zhengran Qiao Lifeng Wu
author_sort	Feifei Fan
title	Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
title_short	Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
title_full	Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
title_fullStr	Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
title_full_unstemmed	Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China
title_sort	using a grey multivariate model to predict impacts on the water quality of the zhanghe river in china
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/ae446d7b12a541c18949c7d826f21924
work_keys_str_mv	AT feifeifan usingagreymultivariatemodeltopredictimpactsonthewaterqualityofthezhangheriverinchina AT zhengranqiao usingagreymultivariatemodeltopredictimpactsonthewaterqualityofthezhangheriverinchina AT lifengwu usingagreymultivariatemodeltopredictimpactsonthewaterqualityofthezhangheriverinchina
_version_	1718443754035085312

Using a grey multivariate model to predict impacts on the water quality of the Zhanghe River in China

Ejemplares similares