Determining groundwater quality using indices and multivariate statistical techniques: a study of Tosham block, Haryana, India

Panghal, V.P.S.; Sharma, Pawan K.; Mona, Sharma; Bhateria, Rachna

doi:10.1007/s10653-021-01120-9

Cited by 12 publications

(8 citation statements)

References 30 publications

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“…The comparison of EWQI and WQI shows that most of the samples of EWQI are more effective than WQI. As compared with past studies undertaken by Kumari & Rai (2020) and Panghal et al (2021), the results obtained in this study are superior thereby, suggesting the importance of successful application of the three machine learning models.…”

Section: Water Quality Index (Wqi)contrasting

confidence: 40%

“…Further groundwater quality is usually determined by the concentration of physical, chemical, and biological parameters (Kumari & Rai 2020). Panghal et al (2021) reported that approximately 82% of the area of Tosham block, Haryana, had poor and very poor water quality for drinking purposes and 18% of area was unsuitable for drinking. It was also observed by Kumari & Rai (2020) that 45.31% of the area of southern Haryana had poor and very poor water quality for drinking purposes in the month of May, state in India.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of groundwater quality indices using machine learning algorithms

Raheja

Goel

Pal

2021

Water Practice and Technology

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The present paper deals with performance evaluation of application of three machine learning algorithms such as Deep neural network (DNN), Gradient boosting machine (GBM) and Extreme gradient boosting (XGBoost) to evaluate the ground water indices over a study area of Haryana state (India). To investigate the applicability of these models, two water quality indices namely Entropy Water Quality Index (EWQI) and Water Quality Index (WQI) are employed in the present study. Analysis of results demonstrated that DNN has exhibited comparatively lower error values and it performed better in the prediction of both indices i.e. EWQI and WQI. The values of Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE = 0.037), Nash–Sutcliffe efficiency (NSE = 0.995), Index of agreement (d = 0.999) for EWQI and CC = 0.975, RMSE = 0.055, NSE = 0.991, d = 0.998 for WQI have been obtained. From variable importance of input parameters, the Electrical conductivity (EC) was observed to be most significant and ‘pH’ was least significant parameter in predictions of EWQI and WQI using these three models. It is envisaged that the results of study can be used to righteously predict EWQI and WQI of groundwater to decide its potability.

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Section: Water Quality Index (Wqi)contrasting

confidence: 40%

Section: Introductionmentioning

confidence: 99%

Prediction of groundwater quality indices using machine learning algorithms

Raheja

Goel

Pal

2021

Water Practice and Technology

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“…The PCA/FA was dependent on the Pearson correlation matrix. It was used to understand the basic relationships between the various water quality parameters of all monitoring sites and to as significant 27,28 so that four PCs with Eigen value greater than one only extracted as factor loadings. As indicated 29 factor loadings are classified as strong, moderate, and weak corresponding to >0.75, 0.75-0.5, and >0.5 respectively.…”

Section: Source Identification Of Monitored Parametersmentioning

confidence: 99%

: Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Kuppusamy¹,

Sridhar²

2022

J. ISAS

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Multivariate statistical methods such as Principal Component Analysis/Factor analysis (PCA/FA), and Cluster Analysis (CA) were used to characterize spatial and temporal variations and to find out the pollution source of 11 lakes of the Kancheepuram district, a northeast district of Tamilnadu in India. Sampling events were conducted during the northeast monsoon (Dec 2015 & 2016) and Pre-monsoon (Oct 2016). The 20 physicochemical water quality parameters were used to analyze the collected water samples.In PCA, four components were demonstrated up to 73.12% of the total variance in the data set. It clarifies that variations in water quality parameters concentration are mainly identified with agricultural, industrial, domestic wastes, and natural factors. The CA showed the formation of three clusters based on the water quality variation at different locations. From the CA data, sampling sites were classified into high, moderate, and least polluted. Among these sites, Kolavai, Nattapettai and Uthiramerur were highly polluted because of rapid urbanization and industrialization. Therefore, these areas need some effective measures to enhance the quality of water.

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“…The GWQI, however, is constrained and cannot identify the sources of contamination 20 . For this reason, multivariate statistical methods were applied.…”

Section: Introductionmentioning

confidence: 99%

“…The comprehensive assessment of groundwater quality requires studying the effect of these factors, and statistical methods are a powerful tool to perform this. Application of multivariate statistical methods for groundwater quality evaluation and identification of the natural and artificial sources of groundwater pollution is widely reported in the literature, For example: 20,[25][26][27][28][29] . The methods mentioned above may indicate the amount of contamination in groundwater; however, they cannot reflect any possible risks that the contaminants pose to human health.…”

Section: Introductionmentioning

confidence: 99%

Multi-criterion approach for assessment of hydrochemical characteristics and human health risk of groundwater in Khartoum State, Sudan

Mohammed

Eltijani

Szabó

et al. 2023

Preprint

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Recently, groundwater has been recognized as one of the primary sources of water supply in Sudan. However, groundwater quality continues to deteriorate due to natural and human-induced activities. This research employed groundwater quality index (GWQI), multivariate statistical methods and human health risk assessment to investigate the suitability of groundwater for domestic uses in northern Khartoum state, Sudan. The groundwater samples were analyzed for eleven physiochemical parameters, including pH, EC, TDS, TH, Cl-, SO4-2, NO3-, Ca+2, Mg+2, Na+, HCO3- and the primary investigation indicated the deviation of these parameters from World Health Organization (WHO) standards. The hydrochemical analysis revealed different groundwater facies with the dominance of Ca-Mg-HCO3 water type. Consequently, the groundwater samples were classified, based on GWQI, into three categories as 76.4 % of the samples fall in the excellent water class, 17.6 % are projected in the good water class, and 5.9 % of groundwater samples are considered unsuitable for human consumption. The multivariate statistical methods, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were applied to determine groundwater quality data's structure and the primary factors influencing groundwater quality. These techniques revealed that groundwater in the study area is mainly controlled by rock-water interaction and agricultural practice. Additionally, they were used to categorize groundwater samples based on their chemical content. As a result, three types of groundwater were identified low, medium, and highly mineralized. In the final stage, the non-carcinogenic human health risk was assessed based on the concentration of NO3- and the obtained hazard quotient for children indicated that 64.7 % of groundwater samples are beyond the permissible limit (1<) and the use of these samples may result in health consequences. As a result, remedial measures are suggested for the sustainable use of groundwater.

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Determining groundwater quality using indices and multivariate statistical techniques: a study of Tosham block, Haryana, India

Cited by 12 publications

References 30 publications

Prediction of groundwater quality indices using machine learning algorithms

Prediction of groundwater quality indices using machine learning algorithms

: Assessment of Surface Water Quality by using Multivariate Statistical Techniques of Various Lakes of Kancheepuram District in Tamil Nadu, India.

Multi-criterion approach for assessment of hydrochemical characteristics and human health risk of groundwater in Khartoum State, Sudan

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