Water Quality Index: A Fuzzy River-Pollution Decision Support Expert System

Nasiri, Fuzhan; Maqsood, Imran; Huang, Guohe; Fuller, Norma

doi:10.1061/(asce)0733-9496(2007)133:2(95)

Cited by 88 publications

(33 citation statements)

References 41 publications

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“…Fuzzy inference systems are a widespread method in the treatment of information for the generation of expert systems in monitoring the water quality (Nasiri et al, 2007;Lermontov et al, 2009;Hatzikos et al, 2009). Roughly speaking, a fuzzy inference system is an algorithm able to convert a strategy or set of linguistic rules in an automated strategy.…”

Section: Descriptionmentioning

confidence: 99%

Fuzzy expert system for the detection of episodes of poor water quality through continuous measurement

Ángulo¹,

Cabestany²,

Rodríguez³

et al. 2012

Expert Systems with Applications

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In order to prevent and reduce water pollution, promote a sustainable use, protect the environment and enhance the status of aquatic ecosystems, this article deals with the application of advanced mathematical techniques designed to aid in the management of records of different water quality monitoring networks. These studies include the development of a software tool for decision support, based on the application of fuzzy logic techniques, which can indicate water quality episodes from the behaviour of variables measured at continuous automatic water control networks. Using a few physicalchemical variables recorded continuously, the expert system is able to obtain water quality phenomena indicators, which can be associated, with a high probability of cause-effect relationship, with human pressure on the water environment, such as urban discharges or diffuse agricultural pollution. In this sense, at the proposed expert system, automatic water quality control networks complement manual sampling of official administrative networks and laboratory analysis, providing information related to specific events (discharges) or continuous processes (eutrophication, fish risk) which can hardly be detected by discrete sampling.

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Section: Descriptionmentioning

confidence: 99%

Fuzzy expert system for the detection of episodes of poor water quality through continuous measurement

Ángulo¹,

Cabestany²,

Rodríguez³

et al. 2012

Expert Systems with Applications

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show abstract

“…These concepts are discussed in depth in Bárdossy (1995), Yen e Langari (1999), Ross (2004), Cruz (2004) and Caldeira et al (2007). The concept of fuzzy sets for modeling water quality was considered by Dahiya (2007), Nasiri et al (2007) Chau (2006, Ocampo-Duque et al (2006), Icaga (2007), and Chang et al (2001), Lermontov et al (2009), Ramesh et al (2010, Taner et al (2011).…”

Section: Fuzzy Inferencementioning

confidence: 99%

“…Methods to integrate several variables related to water quality in a specific index are increasingly needed in national and international scenarios. Several authors have integrated water quality variables into indices, technically called Water Quality Indices (WQIs) (Bolton et al, 1978;Bhargava, 1983;House, 1989;Mitchell, 1996;Pesce and Wunderlin, 1999;Cude, 2001;Liou et al, 2004;Said et al, 2004;Silva and Jardim, 2006;Nasiri et al, 2007). Most are based in a concept developed by the U. S. National Sanitation Foundation (NSF, 2007).…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Water Quality Index for Watershed Quality Analysis and Management

Lermontov¹,

Yokoyama²,

Lermontov³

et al. 2011

Environmental Management in Practice

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“…WQI has sufficient efficiency to assess any changes in groundwater quality. The development of WQI for groundwater quality assessment is described in the several studies (Nasiri et al 2007;Gharibi et al 2012). Moreover, groundwater quality index (GWQI) was used for evaluating groundwater quality in different studies (Gholami et al 2015).…”

Section: Introductionmentioning

confidence: 99%

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

2016

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The process of water quality testing is money/-time-consuming, quite important and difficult stage for routine measurements. Therefore, use of models has become commonplace in simulating water quality. In this study, the coactive neuro-fuzzy inference system (CAN-FIS) was used to simulate groundwater quality. Further, geographic information system (GIS) was used as the preprocessor and post-processor tool to demonstrate spatial variation of groundwater quality. All important factors were quantified and groundwater quality index (GWQI) was developed. The proposed model was trained and validated by taking a case study of Mazandaran Plain located in northern part of Iran. The factors affecting groundwater quality were the input variables for the simulation, whereas GWQI index was the output. The developed model was validated to simulate groundwater quality. Network validation was performed via comparison between the estimated and actual GWQI values. In GIS, the study area was separated to raster format in the pixel dimensions of 1 km and also by incorporation of input data layers of the Fuzzy Network-CANFIS model; the geo-referenced layers of the effective factors in groundwater quality were earned. Therefore, numeric values of each pixel with geographical coordinates were entered to the Fuzzy Network-CANFIS model and thus simulation of groundwater quality was accessed in the study area. Finally, the simulated GWQI indices using the Fuzzy Network-CANFIS model were entered into GIS, and hence groundwater quality map (raster layer) based on the results of the network simulation was earned. The study's results confirm the high efficiency of incorporation of neuro-fuzzy techniques and GIS. It is also worth noting that the general quality of the groundwater in the most studied plain is fairly low.

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Water Quality Index: A Fuzzy River-Pollution Decision Support Expert System

Cited by 88 publications

References 41 publications

Fuzzy expert system for the detection of episodes of poor water quality through continuous measurement

Fuzzy expert system for the detection of episodes of poor water quality through continuous measurement

A Fuzzy Water Quality Index for Watershed Quality Analysis and Management

A method of groundwater quality assessment based on fuzzy network-CANFIS and geographic information system (GIS)

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