Prediction of Water Quality Index Using Neuro Fuzzy Inference System

Sahu, Mrutyunjaya; Mahapatra, S.S.; Sahu, H. B.; Patel, Raj Kishore

doi:10.1007/s12403-011-0054-7

Cited by 41 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…BP Neural Networks was applied for prediction, 4 major pollutants: TS, TH, nitrate radical and fluoride were selected for analyzing and predicting. Water quality data 1985-2005 were applied for network training and data 2006-2011 was used for testing the model; after then, the trained network model was adopted for predicating main pollutants with the predicated results were evaluated by resorting to fuzzy recognition method Karimiet al,2013;Mrutyunjaya et al, 2011;Khashei and Bijari, 2010).…”

Section: Trend Predication For Water Qualitymentioning

confidence: 99%

Evaluation and Predication of Groundwater QualityBased on Fuzzy Model and BP Neural Networks

2016

Rev. Téc. Ing. Univ. Zulia.

View full text Add to dashboard Cite

Groundwater quality is an important indicator to be considered for utilizing water resources; as a result, when developing and protecting water resources, evaluation and predication on the water quality shall be taken as the reference. In this paper, analysis for the over-standard contents in groundwater was conducted for groundwater in Xi"an by applying multiple super-scale method and pollution index method; in addition, fuzzy pattern recognition model was also introduced so as to make comprehensive quality evaluation for the groundwater, then the evaluated results were compared with that of the existed comprehensive index method andprobabilistic neural network model; finally, predication was carried out for progressing trends of the four major pollutants by BP neural networks. The results reveal that total solids (TS), total hardness (TH) and nitrate radical exceeded the standard higher; while the over-standard ratios for fluoride, hexavalent chrome and chloridate content stayed at the average, and low over-standard ratio was found for phenol and arsenic, and pollution indexes of all pollutants were decreasing with fluctuations. Furthermore, Polluted areas were mainly at the urban area and sewage irrigated area in northern suburb. In conclusion, temporal evaluation for groundwater based on fuzzy pattern recognition method proves that groundwater quality in Xi"an had been obviously improved in the duration 1985-2011; the evaluation was more reliable compared with the other two methods. Besides, water quality predication in accordance with BP neutral networks indicates that optimal subordinate degree for most groundwater in Xi"an within 2012-2017 was of level II, which implies that groundwater quality was getting better.

show abstract

Section: Trend Predication For Water Qualitymentioning

confidence: 99%

Evaluation and Predication of Groundwater QualityBased on Fuzzy Model and BP Neural Networks

2016

Rev. Téc. Ing. Univ. Zulia.

View full text Add to dashboard Cite

show abstract

“…e best-fit model was obtained using the Gaussian membership function. Sahu et al [18] employed ANFIS to predict WQI of groundwater.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Two ANFIS Models for Predicting Water Quality Index of River Satluj (India)

Tiwari

Babbar

Kaur

2018

Advances in Civil Engineering

View full text Add to dashboard Cite

Water quality index is the most convenient way of communicating water quality status of water bodies, but its evaluation requires subjectivity in terms of user involvement and dealing with uncertainty. Recently, artificial intelligence algorithms that are appropriate for nonlinear forecasting and also dealing with uncertainties have been applied to various domains of water quality forecasting. This paper focuses on development of a data-driven adaptive neurofuzzy system for the water quality index using a real data set obtained from eight different monitoring stations across River Satluj in northern India. Novelty in the paper lies in the estimation of water quality index using two different clustering techniques: fuzzy C-means and subtractive clustering-based ANFIS and assessing their predictive accuracy. Each model was used to train, validate, and test the index that was obtained from seven water quality parameters including pH, conductivity, chlorides, nitrates, ammonia, and fecal coliforms. The models were evaluated on the basis of statistical performance criteria. Based on the evaluations, it was found that the SC-ANFIS method gave more accurate result as compared to the FCM-ANFIS. The tested model, SC-ANFIS model, was further used to identify those sensitive parameters across various monitoring stations that were capable of causing change in the existing water quality index value.

show abstract

“…; Icaga ; Şen ; Mofarrah & Husain ; Sahu et al . ; Gharibi et al . ; Liu & Zou ; Scannapieco et al .…”

Section: Introductionunclassified

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

Tafreshi

Keshavarzi

2018

Water & Environment J

View full text Add to dashboard Cite

Due to shortage of surface water, resources of ground water in Iran (like many other countries with dry and semi‐dry weather) have turned into the most important and reliable source for water supply. Thus, it is necessary to consider the quality of this resource for drinking purposes. The aim of this study was to assessment suitable drinking water based on a fuzzy approach in Asalem region, Iran. For reduce the uncertainty, fuzzy logic spatial modelling via GIS was applied. To receive these aims, four stages were performed. In stage 1, we calculate the values of eighteen conventional and effective factors used for drinking water quality classification upon analysing the statistical quality data regarding various ions existing in 15 sample wells dug in 2015. In stage 2, interpolate these factors by kriging method via ArcGIS software. In stage 3, parameter standardization with fuzzy membership function were done. And in stage 4, for Aggregation factors, several fuzzy overlay operations were used. Finally, to identify the best operation, the ‘Optimum Index Factor’ was used. Results showed that the ‘GAMMA 0.9’ with highest OIF in most of the factors, is the best overly operation in our study area. According to classified the best operation map, 1.42% of understudied regions are classified in very poor class, 12.48% is grouped in poor class, 78.55% is grouped in fair class, 7.53% is classified in good group and 0.01% is classified as excellent group. In this regard only central parts of the study area have ‘fair’ to ‘good’ and ‘excellent’ groundwater quality for drinking water and in other parts, the quality is within ‘very poor’ and ‘poor’ to ‘fair’.

show abstract

Prediction of Water Quality Index Using Neuro Fuzzy Inference System

Cited by 41 publications

References 28 publications

Evaluation and Predication of Groundwater QualityBased on Fuzzy Model and BP Neural Networks

Evaluation and Predication of Groundwater QualityBased on Fuzzy Model and BP Neural Networks

Performance Evaluation of Two ANFIS Models for Predicting Water Quality Index of River Satluj (India)

Qualitative zoning of groundwater to assessment suitable drinking water using fuzzy logic spatial modelling via GIS

Contact Info

Product

Resources

About