Unveiling groundwater potential zones as catalyst for multidimensional poverty reduction using analytical hierarchical process and geospatial decision support systems (S-DSS) approach in the semiarid region, Jigawa, Nigeria

Gambo, Jibrin; Aliaa binti Roslan, Siti Nur; Mohd Shafrib, Helmi Zulhaidi; Che Ya, Nik Norasma; Yusuf, Yusuf Ahmed

doi:10.1016/j.gsd.2023.101038

Cited by 3 publications

(1 citation statement)

References 96 publications

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“…Multivariate statistical methods like principal component analysis, factor analysis indeed provide valuable tools for analysing complex datasets and gaining insights into the physical and chemical characteristics of groundwater systems in both spatial and temporal dimensions (B Patil et al, 2020). These multivariate statistical methods provide powerful tools for analysing and interpreting complex groundwater datasets, identifying underlying patterns and relationships, and informing groundwater management and decision-making processes (Gambo et al, 2024). By integrating spatial and temporal dimensions, these methods contribute to a better understanding of the physical and chemical characteristics of groundwater systems and support sustainable resource management practices.…”

Section: Introductionmentioning

confidence: 99%

Integrated Geochemical Analysis of Groundwater Quality and Human Health Risks by Using Multivariate Statistical Methods: A Case Study of Mayurbhanj District, Odisha, India

Sahoo,

Panda,

Swain

et al. 2024

Preprint

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Mayurbhanj district is predominantly inhabited by tribal communities. Among the various tribal groups in Odisha, Mayurbhanj alone accommodates 45 distinct categories. These tribal communities primarily rely on natural water sources such as rivers, streams, and tube wells for drinking purposes without undergoing additional purification processes. Hence, investigating the factors affecting groundwater quality is essential to ensure its safety for drinking purposes and mitigate the health risks associated with the consumption of contaminated water. In the present study, groundwater quality of 145 water samples from different sources of Mayurbhanj district was analysed. The geographical coordinates of sample locations and measurements of groundwater quality parameters were used in Geographic Information System software, ArcGIS pro, to construct the spatial distribution and spatial variation maps. Five significant principal components having eigen value greater than 1 with total variance of 73.43. Kaiser-Meyer-Olkin (KMO) test was above 0.5 which shows that data collected from the study area are accurate for analysis. Electrical conductivity, F−, pH and NO3− varies in the range of 42 to 1754 µS/cm, 0.01 to 1.97 mg/l, 5.5 to 7.9 and 0.1 to 21.2 mg/l respectively. The non-carcinogenic health risk assessment indicates that the hazard quotient (HQ) values attributed to fluoride ion and nitrate ion exposure range from 0.43 to 0.46 for children and 0.23 to 0.26 for adults, and from 0.002 to 0.6 for children and 0.001 to 0.3 for adults, respectively. The children are comparatively at slightly more prone to health risk in comparison to adults. Gibbs diagram shows that most of the water samples comes in the region of rock-water interaction dominance in Gibbs plot of TDS vs chloride ion concentration. In the loading biplot for the study area, the first principal component in the horizontal axes has positive coefficients for carbonate, chloride, bicarbonate, total alkalinity, calcium hardness, magnesium hardness, total dissolved solids, electrical conductivity, fluoride. TDS has positive correlation with EC (0.98), chloride (0.525), nitrate (0.445), sulphate (0.445), total hardness (0.438), total alkalinity (0.524), carbonate (0.528) and bicarbonate (0.535). The software used for statistical study are, Minitab, Origin and SPSS. The results of this study would be useful for the Government and policy makers to provide safe and quality drinking water to the tribal community.

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Section: Introductionmentioning

confidence: 99%

Integrated Geochemical Analysis of Groundwater Quality and Human Health Risks by Using Multivariate Statistical Methods: A Case Study of Mayurbhanj District, Odisha, India

Sahoo,

Panda,

Swain

et al. 2024

Preprint

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Poverty reduction and the sustainable development goals: a bibliometric study focused on the Global South

Majeed,

Abbas,

Kadhim

et al. 2024

Development in Practice

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Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

Tavakoli,

Motlagh,

Sayadi

et al. 2024

Water

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Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as a result of the rapid conversion of rural agricultural land to urban areas under chronic drought conditions. This study aims to enhance Groundwater Pollution Risk (GwPR) mapping by integrating the DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), and Multivariate Adaptive Regression Splines (MARS), alongside hydrogeochemical investigations, to promote sustainable water management in Kerman Province. The RF model achieved the highest accuracy with an Area Under the Curve (AUC) of 0.995 in predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), and GLM (0.887). The RF-based map identified new high-vulnerability zones in the northeast and northwest and showed an expanded moderate vulnerability zone, covering 48.46% of the study area. Analysis revealed exceedances of WHO standards for total hardness (TH), sodium, sulfates, chlorides, and electrical conductivity (EC) in these high-vulnerability areas, indicating contamination from mineralized aquifers and unsustainable agricultural practices. The findings underscore the RF model’s effectiveness in groundwater prediction and highlight the need for stricter monitoring and management, including regulating groundwater extraction and improving water use efficiency in riverine aquifers.

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Unveiling groundwater potential zones as catalyst for multidimensional poverty reduction using analytical hierarchical process and geospatial decision support systems (S-DSS) approach in the semiarid region, Jigawa, Nigeria

Cited by 3 publications

References 96 publications

Integrated Geochemical Analysis of Groundwater Quality and Human Health Risks by Using Multivariate Statistical Methods: A Case Study of Mayurbhanj District, Odisha, India

Integrated Geochemical Analysis of Groundwater Quality and Human Health Risks by Using Multivariate Statistical Methods: A Case Study of Mayurbhanj District, Odisha, India

Poverty reduction and the sustainable development goals: a bibliometric study focused on the Global South

Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

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