Geographic information system-based groundwater quality assessment for drinking and irrigation purposes in transboundary aquifers of River Ravi, India

Awasthi, Ashima; Rishi, Madhuri S.; Khosla, Ashu; Panjgotra, Shivali

doi:10.1007/s11356-022-24642-w

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…In recent years, the Geographic information system (GIS) has developed as a powerful tool for collecting, analyzing, and visually representing spatial data. This data is then used to make informed decisions across several water resource fields (Awasthi et al, 2023). Omeka et al, (2023) used GIS tools to evaluate the water quality for irrigation purposes in southeastern parts of Obubra LGA, Nigeria, and the results specified that GIS-AHP techniques are very reliable for understanding the overall quality of groundwater.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Raheja,

Goel,

Pal

2024

Preprint

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The main objective of the present study is to evaluate the groundwater quality for irrigation purposes in the central-western part of Haryana state (India). For this, 272 groundwater samples were collected during the Pre- and Post-monsoon periods in 2022. Several indices, including Sodium Absorption Ratio (SAR), Permeability Index (PI), Sodium Percentage (Na %), Kelly Ratio (KR), Magnesium Adsorption Ratio (MAR), and Irrigating water quality index (IWQI) were derived. The results in terms of SAR, Na%, and KR values indicate that the groundwater is generally suitable for irrigation. On the other hand, PI and MAR exceeded the established limits, primarily showing issues related to salinity and magnesium content in the groundwater. Furthermore, according to the groundwater quality assessment based on the IWQI classification, 47.06% and 25% of the total collected samples fell under the "Severe Restriction for irrigation" category during the Pre-monsoon and Post-monsoon periods, respectively. Spatial variation maps indicate that water quality in the western portion of the study area is unsuitable for irrigation during both periods. Three Machine learning (ML) algorithms, namely Random forest (RF), Support vector machine (SVM), and Extreme Gradient Boosting (XGBoost) were integrated and validated to predict the IWQI. The results revealed that the XGBoost with Random searchachieves the best prediction performances. The approaches established in this study have been confirmed to be cost-effective and feasible for groundwater quality, using hydrochemical parameters as input variables, and highly beneficial for water resource planning and management.

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Section: Graphical Abstract Introductionmentioning

confidence: 99%

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Raheja,

Goel,

Pal

2024

Preprint

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“…All life forms need water as a primary natural resource in order to survive. Groundwater makes up 29.9% of the world's freshwater resources, whereas only a mere 0.26% of the total freshwater is found in lakes, reservoirs, and river systems (Awasthi et al, 2023). The availability of water resources is necessary for the social and economic conditions to grow in a sustainable manner across the world.…”

Section: Introductionmentioning

confidence: 99%

Hydrogeochemistry and Groundwater Quality Assessment in Gujarat

Dalsania,

Pandey,

Sircar

et al. 2023

Preprint

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Assessing water quality is crucial for ensuring the availability of safe drinking water, sustainable agricultural practices, and safe industrial utilization. This paper presents a comprehensive study conducted over a five-year period in Lasundra, Savarkundla, Saputara, and Junagadh to determine whether the water is suitable for drinking, irrigation, and industrial use. The objective is to offer insightful information on the long-term fluctuations and trends in water quality in these areas. The collected data were utilized to calculate water quality indices specifically tailored for drinking, irrigation, and industrial purposes. The essential Water Quality Indices such as Ryznar Stability Index (RSI) and Langelier Saturation Index (LSI) have been examined for industrial application and primary indices like Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR), Potential Salinity (PS) and Permeability Index (PI) have been examined for drinking and irrigational applications of groundwater. These indices provided a quantitative assessment of overall water quality, enabling comparative analysis across the four study areas. Furthermore, advanced visualization techniques such as Piper, Wilcox, and Schoeller plots were illustrated to understand the changes in physiochemical characteristics over a five-year period. The results can help policymakers, water resource managers, and local communities in implementing appropriate measures to safeguard water resources, improve treatment processes, and enhance the water's quality for use in industrial, agricultural, and drinking purposes.

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A holistic review on the assessment of groundwater quality using multivariate statistical techniques

Patel

Pandya

Shah

2023

Environ Sci Pollut Res

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Geographic information system-based groundwater quality assessment for drinking and irrigation purposes in transboundary aquifers of River Ravi, India

Cited by 5 publications

References 51 publications

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Application of Geospatial and Machine Learning Algorithms for Groundwater Quality Prediction Used for Irrigation Purposes

Hydrogeochemistry and Groundwater Quality Assessment in Gujarat

A holistic review on the assessment of groundwater quality using multivariate statistical techniques

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