Prediction of ground water quality index to assess suitability for drinking purposes using fuzzy rule-based approach

Gorai, Amit Kumar; Iqbal, Jawed

doi:10.1007/s13201-014-0241-3

Cited by 38 publications

(7 citation statements)

References 30 publications

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“…Over the past decade, several research initiatives have been aimed at creating and enhancing water quality prediction models [45][46][47]. Recently, machine learning and data-driven results have shown promising results in the development of precise water quality estimation models [48,49].…”

Section: Introductionmentioning

confidence: 99%

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Elsayed

Hussein

Moghanm

et al. 2020

Water

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Under sustainable development conditions, the water quality of irrigation systems is a complex issue which involves the combined effects of several surface water management parameters. Therefore, this work aims to enhance the surface water quality assessment and geochemical controlling mechanisms and to assess the validation of surface water networks for irrigation using six Water Quality Indices (WQIs) supported by multivariate modelling techniques, such as Principal Component Regression (PCR), Support Vector Machine Regression (SVMR) and Stepwise Multiple Linear Regression (SMLR). A total of 110 surface water samples from a network of surface water cannels during the summers of 2018 and 2019 were collected for this research and standard analytical techniques were used to measure 21 physical and chemical parameters. The physicochemical properties revealed that the major ions concentrations were reported in the following order: Ca2+ > Na+ > Mg2+ > K+ and alkalinity > SO42− > Cl− > NO3− > F−. The trace elements concentrations were reported in the following order: Fe > Mn > B > Cr > Pb > Ni > Cu > Zn > Cd. The surface water belongs to the Ca2+-Mg2+-HCO3− and Ca2+-Mg2+-Cl−-SO42− water types, under a stress of silicate weathering and reverse ion exchange process. The computation of WQI values across two years revealed that 82% of samples represent a high class and the remaining 18% constitute a medium class of water quality for irrigation use with respect to the Irrigation Water Quality (IWQ) value, while the Sodium Percentage (Na%) values across two years indicated that 96% of samples fell into in a healthy class and 4% fell into in a permissible class for irrigation. In addition, the Sodium Absorption Ratio (SAR), Permeability Index (PI), Kelley Index (KI) and Residual Sodium Carbonate (RSC) values revealed that all surface water samples were appropriate for irrigation use. The PCR and SVMR indicated accurate and robust models that predict the six WQIs in both datasets of the calibration (Cal.) and validation (Val.), with R2 values varying from 0.48 to 0.99. The SMLR presented estimated the six WQIs well, with an R2 value that ranged from 0.66 to 0.99. In conclusion, WQIs and multivariate statistical analyses are effective and applicable for assessing the surface water quality. The PCR, SVMR and SMLR models provided robust and reliable estimates of the different indices and showed the highest R2 and the highest slopes values close to 1.00, as well as minimum values of RMSE in all models.

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Section: Introductionmentioning

confidence: 99%

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Elsayed

Hussein

Moghanm

et al. 2020

Water

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“…Due to the ability of the fuzzy method to reduce uncertainties and its other advantages, some other researchers have tried to combine or compare the traditional methods with the fuzzy inference system so that a group of researchers have used both the drinking water quality index and the fuzzy inference system to compare or combine the results and have better and acceptable results. They have achieved more than the previous obedience [20][21][22][23][24][25]5].…”

Section: Introdoctionmentioning

confidence: 90%

Evaluation and zonation of groundwater quality in terms of drinkability, using Fuzzy Logic and Schoeller deterministic method. (Northern Dezful - Andimeshk Plain)

Shakour,

Chitsazan,

Mirzaee

2023

Preprint

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In the Northern Dezful-Andimeshk plain, the primary source of drinking water is groundwater, which has got low quality in some places. Thus, groundwater quality in this area needs an accurate evaluation. In this research, fuzzy and Schoeller deterministic method assessed the Northern Dezful-Andimeshk plain drinking water, and the results of both methods were used to zone and compare the drinking quality of the plain groundwater in the Arc GIS environment. The deterministic method classifies drinking water quality from good to bad. According to this method, 56.81% of the total area of the plain has good quality, 20.83% of the total area has acceptable, 18.77% of the total area has inappropriate quality, and 3.57% of the total area has bad quality. However, in the fuzzy method, groundwater in 21.6% of the total area of the plain with a confidence percentage of 70–81% is desirable, in 75.23% of the total area of the plain with a confidence percentage of 32–70% is acceptable, and 3.69% of the total area of the plain with drinking confidence of 20–22% is nonacceptable. The results showed that the fuzzy method is better than Schoeller deterministic method because it reduces uncertainties and increases accuracy in the evaluation and increases flexibility in the threshold limits of water quality parameters. and provides a quantitative and qualitative assessment of drinking water, also the zoning map obtained from this method is more compatible with environmental and pollution realities.

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“…Fuzzy logic to analyse the suitability of drinking water considering with 11 physic-chemical parameters such as pH, Ca, Mg, Fe, hardness, alkalinity, dissolved solids, fluoride, As, sulphate, and nitrates was proposed by Gorai et al (2014). An evaluation method of water quality for drinking purpose using Fuzzy Drinking Water Quality Index (FDWQI) was proposed by Mishra and Jha (2014).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy classifier model to know the sustainability of aquatic organisms and to forecast the aqua farmers

Palani

Balasubramaniyan

Devaraj

2020

Environ Sci Pollut Res

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This research work focuses to develop a fuzzy classifier model, to know the sustainability of aquatic animals and/or plants by assessing the aqua pond water quality. This model categorizes the water quality of aqua pond into four levels like as normal, acceptable, abnormal, and dangerous based on the numerical values of physical limits. The developed model is useful to forecast the pond water quality by the aqua farmers to keep up within the acceptable limits at the earliest. Data collected from five ponds are used to develop the fuzzy classifier model. The output of this model is validated using a known set of sample data. This model yields high classification performance against variation in aqua pond water quality parameters and also provides the status of pond water continuously along with a remedy to keep up the water quality.

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Prediction of ground water quality index to assess suitability for drinking purposes using fuzzy rule-based approach

Cited by 38 publications

References 30 publications

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Application of Irrigation Water Quality Indices and Multivariate Statistical Techniques for Surface Water Quality Assessments in the Northern Nile Delta, Egypt

Evaluation and zonation of groundwater quality in terms of drinkability, using Fuzzy Logic and Schoeller deterministic method. (Northern Dezful - Andimeshk Plain)

Fuzzy classifier model to know the sustainability of aquatic organisms and to forecast the aqua farmers

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