A novel approach in water quality assessment based on fuzzy logic

Gharibi, Hamed; Mahvi, Amir Hossein; Nabizadeh, Ramin; Arabalibeik, Hossein; Yunesian, Masud; Sowlat, Mohammad H.

doi:10.1016/j.jenvman.2012.07.007

Cited by 157 publications

(89 citation statements)

References 31 publications

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“…The water quality index, developed by the National Sanitation Foundation(NSF) [57,58] is an important tool to interpret the overall status of water quality in a simple and understandable manner. Water quality index (WQI) has been extensively applied to monitor water quality in recent years, and is indeed a practical method considering critical environmental variables, which represent the pollution conditions of a water body [15,[59][60][61] .…”

Section: Assessment Methods Of Surface Watermentioning

confidence: 99%

Hydrochemical Characteristics and Water Quality Assessment of Surface Water at Xiahe County in Tibetan Plateau Pastoral of China

Zhang

Wang

Yousaf

et al. 2016

Preprint

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Water quality assessment in pastoral of Tibetan Plateau, which is water sources for about 40% of world's population and the runoff-yield area of Yellow rivers, is very important. In this paper, Xiahe county which belongs to Tibetan Plateau pastoral was investigated. Six parameters(via, chloride, COD, ammonia nitrogen, nitrate, fluoride, sulfate) were selected to assess the water quality and health degree by using fuzzy comprehensive evaluation methods. The hydrochemical type in surface water was of HCO3 --Mg 2+ -C a2+ type. The cations and anions in surface water were mainly from weathering and dissolution of carbonate rock. Results showed that the water quality in all 69 sampling sites was all of class Ⅰ. The integrated health degree reached more than 0.85 and the health rate were 100%. Although ammonia nitrogen was regarded as the main contamination factor, but it had a little effect on the entire body of water. Overall, the surface water qualities of most samples in Xiahe County was good condition.

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Section: Assessment Methods Of Surface Watermentioning

confidence: 99%

Hydrochemical Characteristics and Water Quality Assessment of Surface Water at Xiahe County in Tibetan Plateau Pastoral of China

Zhang

Wang

Yousaf

et al. 2016

Preprint

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“…Neuro-fuzzy inference systems consist of four main components comprising: fuzzifier input, fuzzy knowledge base, inference engine and defuzzyfier output. At the beginning of processing, fuzzifier, as one of main components of the fuzzy inference system convert observed data to acceptable form of fuzzy membership functions (MFs) and then fuzzifier outputs are used as fuzzy inference productive inputs (Tay and Zhang 2000;Gharibi et al 2012). The major components of CANFIS are (a) a fuzzy axon, which applies membership functions to the inputs and (b) a modular network that applies functional rules to the inputs (Heydari and Talaee 2011).…”

Section: Coactive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…Therefore, to access this important purpose, namely to make the best and optimal use of the available water, it is necessary to extent a comprehensive index that is representative of the overall water quality . First time, the water quality index (WQI) was developed by the national sanitation foundation (NSF) as a standard index for assessment of the water quality and also as a technique of rating water quality (Ott 1978;Al-hadithi 2012;Gharibi et al 2012). WQI has sufficient efficiency to assess any changes in groundwater quality.…”

Section: Introductionmentioning

confidence: 99%

“…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%

“…In order to eliminate the problem of the number of parameters and related limitations in water quality assessment, Ocampo-Duque et al (2007) developed the fuzzy water quality index (FWQ). In recent years, artificial intelligence (AI) computational methods, such as the neuro-fuzzy systems have been increasingly applied to environmental issues (Chau 2006;Gharibi et al 2012). The neuro-fuzzy systems are the result of the combination of neural networks and fuzzy logic (Zadeh 1965;Pramanik and Panda 2009).…”

Section: Introductionmentioning

confidence: 99%

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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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