Development of a dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems

Gharibi, Hamed; Sowlat, Mohammad H.; Mahvi, Amir Hossein; Mahmoudzadeh, H; Arabalibeik, Hossein; Keshavarz, Morteza; Karimzadeh, Narges; Hassani, Ghasem

doi:10.1016/j.ecolind.2012.02.015

Cited by 50 publications

(19 citation statements)

References 28 publications

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“…Therefore, great attention has been paid to the development of environmental indices based on fuzzy logic (Gharibi et al, 2012;Lu et al, 1999;Mirabbasi et al, 2008;Sasikumar and Mujumdar, 1998;Sowlat et al, 2011), especially when applied to the analysis of water quality (Chang et al, 2001;Icaga, 2007;Lermontov et al, 2009;Liou et al, 2003;Ocampo-Duque et al, 2006, 2007. However, since the number of parameters included in the WQI and the above mentioned fuzzy indices is limited, these indices do not seem to be representative of the overall water quality and an individual parameter may have a considerable impact on the final index value (Ocampo-Duque et al, 2006).…”

Section: Introductionmentioning

confidence: 96%

A novel approach in water quality assessment based on fuzzy logic

Gharibi

Mahvi

Nabizadeh

et al. 2012

Journal of Environmental Management

Self Cite

155

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Section: Introductionmentioning

confidence: 96%

A novel approach in water quality assessment based on fuzzy logic

Gharibi

Mahvi

Nabizadeh

et al. 2012

Journal of Environmental Management

Self Cite

155

View full text Add to dashboard Cite

“…However, most WQIs have been developed for general water purposes, such as Horton's Index, the National Sanitation Foundation (NSF) Index, the British Columbia Water Quality Index, the Oregon Water Quality Index, the Overall Index of Pollution, the River Pollution Index (RPI), and the Universal Water Quality Index (Liou et al 2004). Only a few WQIs have been developed based on specific water-use purposes, such as the water supply or drinking water (Mohebbi et al 2013;Wanda et al 2012;Ramesh et al 2010), recreation or water sports (Smith and Daviescolley 1992;Anderson et al 2012;Kamizoulis and Saliba 2004;Marino et al 1995;Nagels et al 2001), public bathing (Smith et al 1991;Kamizoulis and Saliba 2004;Kay 1988), estuaries or coastal water systems (Garcia-Marin et al 2013;Neto et al 2013), irrigation or agriculture (Goss and Richards 2008;Herricks and Suen 2006;Al-Bassam and Ai-Rumikhani 2003;Wang et al 2011), livestock (Gharibi et al 2012), and marine shrimp culture (Ferreira et al 2011). Many government agencies have applied WQI to develop their own standards for various water purposes, which differ with regard to the specific terms used for the classification schemes and in the selection of indicator parameters (Bordalo et al 2006;Sarkar and Abbasi 2006;Suen and Herricks 2009;Bordalo et al 2001).…”

Section: Introductionmentioning

confidence: 99%

Aquaculture Water Quality Index: a low-cost index to accelerate aquaculture development in Indonesia

Tallar

Suen

2015

Aquacult Int

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Due to a lack of the related data, there were no simple water indexes available for indicating water quality. Motivated by the fact that many lakes and reservoirs in Indonesia have been polluted because of aquacultural activity, this paper proposed a Water Quality Index, called the Aquaculture Water Quality Index (AWQI), and presented the development of practical tool to aid the development of aquaculture in Indonesia. Specific purposes included: (a) providing a brief summary of the assessment results of the existing WQI; (b) developing the AWQI method for applying in an aquacultural context; (c) implementing the AWQI method in Depok Area (Indonesia) as study area. An AHP method was also processed to define the weights of selected water quality parameters: DO, ammonia, pH, and fecal coliform. The AWQI had several features: (a) a low-cost water quality monitoring program that could be used by developing countries; (b) a useful tool for water resource agencies, especially for local agencies, to manage waterbodies and to raise public awareness of water pollution for its data, as its data could be easily understood and interpreted.

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“…Singh et al (2011) studied the current status of water quality in India and developed a WQI for groundwater and aquifers in that region. Gharibi et al (2012) created a WQI for the water supply for dairy cattle in Iran. Finally, Varbiró et al (2012) used limnological variables associated with pollution indices to characterize water bodies in Hungary.…”

Section: Discussionmentioning

confidence: 99%

A global scientific literature of research on water quality indices: trends, biases and future directions

2014

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Aim: Water quality has been the subject of many recent studies, moreover, the physical, chemical and biological parameters of water are used to investigate water quality and can be combined into a single index, the Water Quality Index (WQI), for use by water resource managers and the general public. The aim of this study was to use scientometrics to evaluate how water quality has been addressed in the international scientific literature. Method: For the quantitative analysis of the publications on WQI, we used the search database SCOPUS (http://www.scopus.com). The search was performed using the words "QUALIT* WATER* INDEX*" in papers published in all databases (through 2011). Results: We found 554 articles that dealt with the use of WQI the number of publications has increased significantly over the last 20 years. India had the most studies, with 177 articles, followed by China, Brazil and the United States. These four countries together published 57% of studies on WQI. We generated 15 linear models to explain the number of publication by study sit (country). According to the Akaike Information Criterion (AIC), the best model to explain the number of publications by country was the model that combined Sanitation and Public Supply. Conclusion: Finally, this paper presents the state of scientific literature on WQI and demonstrates the growing interest of the scientific community in this issue, which is certainly due to the importance of the quantity and quality of water for human supply, economics, health and the conservation of water resources.Keywords: scientometrics, river, stream, NSF, sanitation, water supply.Resumo: Objetivo: A qualidade da água tem sido objeto de muitos estudos recentes, além disso, os parâmetros físicos, químicos e biológicos da água são usados para investigar a qualidade da água e podem ser combinados em um único índice, um Índice de Qualidade de Água (IQA), para uso de recursos hídricos gestores e público em geral. O objetivo deste estudo foi utilizar a cienciometria para avaliar como a qualidade da água tem sido abordada na literatura científica internacional. Método: Para a análise quantitativa das publicações sobre IQA, foi utilizada a pesquisa de banco de dados SCOPUS (http://www.scopus.com). A pesquisa foi realizada utilizando as palavras "QUALIT* WATER* INDEX*", em artigos publicados em todos os bancos de dados (até 2011). Resultado: Encontramos 554 artigos que tratavam do uso de IQA, e o número de publicações aumentou significativamente ao longo dos últimos 20 anos. A Índia teve o maior número de estudos, com 177 artigos, seguidos por China, Brasil e Estados Unidos. Esses quatro países juntos publicados 57% dos estudos sobre IQA. Geramos 15 modelos lineares para explicar o número de publicação de estudo por país. De acordo com o Critério de Informação de Akaike (AIC), o melhor modelo para explicar o número de publicações por país foi o modelo que combinou Saneamento e Abastecimento Público. Discussão: Finalmente, este trabalho apresenta o estado da literatura científica...

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Development of a dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems

Cited by 50 publications

References 28 publications

A novel approach in water quality assessment based on fuzzy logic

A novel approach in water quality assessment based on fuzzy logic

Aquaculture Water Quality Index: a low-cost index to accelerate aquaculture development in Indonesia

A global scientific literature of research on water quality indices: trends, biases and future directions

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