Integrated groundwater quality analysis using Water Quality Index, GIS and multivariate technique: a case study of Guwahati City

Hinge, Gilbert; Bharali, Biswadeep; Baruah, Anupal; Sharma, Ashutosh

doi:10.1007/s12665-022-10544-0

Cited by 25 publications

(7 citation statements)

References 55 publications

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“…The WQI technique is a helpful tool for explaining water quality to the general public, especially policymakers. It is a clear tool that enables the integration of water parameters that are regarded important to the water's quality (Hinge et al., 2022). The Ganol River's overall water quality was evaluated using a group of 12 commonly used water quality indicators, including pH, EC, TDS, TSS, Cl, Alkalinity, Hardness as CaCO 3 , Sulphate, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Turbidity, Nitrate, Calcium, Magnesium, and Fluoride.…”

Section: Methodsmentioning

confidence: 99%

“…India has abundant water resources, including a network of rivers and snow cover in the Himalayan range, which can supply the country's diverse water requirements. However, due to the country's rapid population growth and the need to meet rising irrigation, human, and industrial consumption demands, limited water supplies in many parts of the country are depleting, and water quality is deteriorating (Hinge et al., 2022). The discharge of untreated sewage and industrial effluents is the major cause of water contamination which is currently a major concern in India and around the world (Alam & Singh, 2023).…”

Section: Introductionmentioning

confidence: 99%

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An integrated study of water quality in the Ganol River Basin, India: Application of hydro‐chemical, multivariate statistical, and water quality index techniques

Das,

Bharali

2024

Environmental Quality Mgmt

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The Ganol River, a vital water source for Meghalaya, India, faces significant environmental degradation due to human activities, primarily the improper disposal of domestic and commercial wastes. This study aims to assess the current water quality of the Ganol River through hydro‐chemical analysis, multivariate statistical methods, and Water Quality Index (WQI) analysis. Over a span of 6 years (2014–2020), monthly water samples were collected from ten source points or stations along the river. The findings revealed that some environmental variables had mean values below the standard limit, while others, notably dissolved oxygen (DO) and turbidity, had higher values. The Pearson correlation coefficients was used to establish the closeness or relationship within and between physico‐chemical characteristics, with some displaying strong positive connections and others showing moderate to negative correlations. The study identified several key environmental contaminants, including conductivity, BOD, COD, TSS, D.O, chloride, TDS, sulphate, hardness, calcium, magnesium, potassium, fecal coliform, and total coliform, based on their highest factor loading values. Such contaminants are significantly impacting aquatic life and the overall ecosystem of the Ganol River. Moreover, the Water Quality Index (WQI) assessment indicated a continuous decline annually in the water quality of the Ganol River. This decline poses a severe threat to the river's sustainability and the well‐being of the communities relying on it for various purposes.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An integrated study of water quality in the Ganol River Basin, India: Application of hydro‐chemical, multivariate statistical, and water quality index techniques

Das,

Bharali

2024

Environmental Quality Mgmt

Self Cite

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“…The loadings for these variables are depicted in Table 4, where figures approaching ±1 reveal a significant linkage between the factor and the indicators. These figures are classified into three levels of association: strong (greater than ±0.75), moderate (between ±0.75 and ±0.50), and weak (from ±0.50 to ±0.30) as outlined by Hinge et al, (2022).…”

Section: Principal Component Analysismentioning

confidence: 99%

Employing Machine Learning Approaches and Multivariate Analysis to Assess Groundwater Quality for Irrigation in the Mornag Plain, Tunisia

Hfaiedh,

Gaagai,

Moussa

et al. 2024

Preprint

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The crucial assessment of water quality in the Mornag Plain, Tunisia, is fundamental for reservoir management, ensuring suitability for consumption, and preserving environmental integrity. Employing a diverse range of methodologies, such as water quality indices (WQIs), statistical analyses, geographic information systems (GIS), and decision tree (DT) model, provided a nuanced understanding of the compositional attributes of groundwater designated for irrigation. Detected categories of water, for instance Na-Cl, Ca-Mg-SO4, and a combination of Ca-Mg-Cl/SO4, displayed unique chemical signatures. These patterns were shaped by diverse processes including interactions between water and rock, the breakdown of dolomite, the concentration of minerals through evaporation, the swapping of ions, and human impact. Evaluating groundwater's suitability for irrigation purposes by employing measures like Na%, SAR, SSP, and MH, demonstrated that a significant portion of the samples conformed to approved norms. However, the analysis revealing 65.6% of the IWQI and every instance of PS falling within the spectrum of high to severe constraints, as well as ranging from detrimental to unsatisfactory classes, underscores the obstacles in sustaining superior irrigation water standards. The predictive model, DT, demonstrated robust results in forecasting all water quality indices. The DT-HyC-9 model stood out as the top performer in prediction accuracy, demonstrating a robust correlation with prime factors affecting IWQI, as shown by substantial R2 metrics in both the training and evaluation phases. Likewise, the DT-HyC-3 approach showcased remarkable precision in forecasting IWQI, highlighting the critical role of three essential factors. These results provide crucial guidance for policymakers engaged in managing water quality, laying the groundwork for the sustainable management of water resources within the Mornag Plain. The integration of advanced methodologies and predictive models enhances the understanding of groundwater dynamics, facilitating informed decision-making for the region's water sustainability.

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“…The application of PCA in hydrochemical studies is an effective and valuable tool that can provide useful information on the complex associations and relationships between geochemical variables [61][62][63]. Moreover, the PCA method is performed for identifying and expressing similarities and differences, and aggregating samples according to their hydrochemical properties.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Applying Multivariate Analysis and Machine Learning Approaches to Evaluating Groundwater Quality on the Kairouan Plain, Tunisia

Salem,

Gaagai,

Ben Slimene

et al. 2023

Water

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In the Zeroud basin, a diverse array of methodologies were employed to assess, simulate, and predict the quality of groundwater intended for irrigation. These methodologies included the irrigation water quality indices (IWQIs); intricate statistical analysis involving multiple variables, supported with GIS techniques; an artificial neural network (ANN) model; and an XGBoost regression model. Extensive physicochemical examinations were performed on groundwater samples to elucidate their compositional attributes. The results showed that the abundance order of ions was Na+ > Ca2+ > Mg2+ > K+ and SO42− > HCO3− > Cl−. The groundwater facies reflected Ca-Mg-SO4, Na-Cl, and mixed Ca-Mg-Cl/SO4 water types. A cluster analysis (CA) and principal component analysis (PCA), along with ionic ratios, detected three different water characteristics. The mechanisms controlling water chemistry revealed water–rock interaction, dolomite dissolution, evaporation, and ion exchange. The assessment of groundwater quality for agriculture with respect IWQIs, such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate (RSC), revealed that the domination of the water samples was valuable for agriculture. However, the IWQI and PS fell between high-to-severe restrictions and injurious-to-unsatisfactory. The ANN and XGBoost regression models showed robust results for predicting IWQIs. For example, ANN-HyC-9 emerged as the most precise forecasting framework according to its outcomes, as it showcased the most robust link between prime attributes and IWQI. The nine attributes of this model hold immense significance in IWQI prediction. The R2 values for its training and testing data stood at 0.999 (RMSE = 0.375) and 0.823 (RMSE = 3.168), respectively. These findings indicate that XGB-HyC-3 emerged as the most accurate forecasting model, displaying a stronger connection between IWQI and its exceptional characteristics. When predicting IWQI, approximately three of the model’s attributes played a pivotal role. Notably, the model yielded R2 values of 0.999 (RMSE = 0.001) and 0.913 (RMSE = 2.217) for the training and testing datasets, respectively. Overall, these results offer significant details for decision-makers in managing water quality and can support the long-term use of water resources.

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Integrated groundwater quality analysis using Water Quality Index, GIS and multivariate technique: a case study of Guwahati City

Cited by 25 publications

References 55 publications

An integrated study of water quality in the Ganol River Basin, India: Application of hydro‐chemical, multivariate statistical, and water quality index techniques

An integrated study of water quality in the Ganol River Basin, India: Application of hydro‐chemical, multivariate statistical, and water quality index techniques

Employing Machine Learning Approaches and Multivariate Analysis to Assess Groundwater Quality for Irrigation in the Mornag Plain, Tunisia

Applying Multivariate Analysis and Machine Learning Approaches to Evaluating Groundwater Quality on the Kairouan Plain, Tunisia

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