Hydrochemical characteristics and sources of groundwater pollution in Soubré and Gagnoa counties, Côte d’Ivoire

Adjéï Kouacou, Boadou; Anornu, Geophrey; Adiaffi, Bernard; Gibrilla, Abass

doi:10.1016/j.gsd.2024.101199

Groundwater for Sustainable Development

2024

DOI: 10.1016/j.gsd.2024.101199

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Hydrochemical characteristics and sources of groundwater pollution in Soubré and Gagnoa counties, Côte d’Ivoire

Boadou Adjéï Kouacou,

Geophrey Anornu,

Bernard Adiaffi

et al.

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Cited by 3 publications

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Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques

Kazapoe,

Sagoe,

Abu

2024

Discov Water

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The quality of groundwater resources in artisanal mining districts in Ghana is under threat due to pollution; rendering the resource unsafe for drinking and irrigation purposes. This makes the assessment of the quality of groundwater resources a relevant aspect of groundwater studies as it informs decision making and monitoring. This study adopts 3 Machine Learning (ML) models, Support Vector Regression (SVR), Gradient Boost Regression (GBR), and Artificial Neural Network (ANN), to evaluate a variety of irrigation water quality metrics such as Sodium Percentage (Na%), Soluble Sodium Percentage (SSP), Sodium Adsorption Ratio (SAR), Residual Sodium Carbonate (RSC), Permeability Index (PI), Pollution Index of Groundwater (PIG), Kelly’s Ratio (KR), and Magnesium Hazard (MH). 105 samples were collected from a mining area in Northern Ghana and analysed through traditional methods. The Irrigation Water Quality Indices (IWQIs) demonstrate that all water samples are suitable for use as irrigable water with the exception of MH, Na%, PI, and PIG which revealed that 69.52%, 8.57%, 29.52%, and 3.81% are inappropriate for irrigation. SVR, GBR and ANN were used to establish important factors that may influence IWQIs in the area. The measured data was used as independent variables, and the derived IWQIs, the dependent variables. The results revealed that ANN, GBR, and SVR are all viable options for the prediction of IWQIs, but GBR exhibited variable performance in some indices making it lack consistency and thus falls a bit short compared to ANN and SVR. SVR models overall performed best with SVR-RSC having the highest accuracy.

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Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques

Kazapoe,

Sagoe,

Abu

2024

Discov Water

View full text Add to dashboard Cite

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Spatial variability, source identification, and partitioning of groundwater constituents in a typical lakeside plain on Yungui Plateau

Hu,

Xiao,

Wang

et al. 2024

Process Safety and Environmental Protection

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Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

Liu,

Li,

Zhang

et al. 2024

Water

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This study employed hydrochemical data, traditional hydrogeochemical methods, inverse hydrogeochemical modeling, and unsupervised machine learning techniques to explore the hydrogeochemical traits and origins of groundwater in the Changbai Mountain region. (1) Findings reveal that predominant hydrochemical types include HCO3−Ca·Mg, HCO3−Ca·Na·Mg, HCO3−Mg·Na, and HCO3−Na·Mg. The average metasilicic acid content was found to be at 49.13 mg/L. (2) Rock weathering mechanisms, particularly silicate mineral weathering, primarily shape groundwater chemistry, followed by carbonate dissolution. (3) Water-rock interactions involve volcanic mineral dissolution and cation exchange adsorption. Inverse hydrogeochemical modeling, alongside analysis of the widespread volcanic lithology, underscores the complexity of groundwater reactions, influenced not only by water-rock interactions but also by evaporation and precipitation. (4) Unsupervised machine learning, integrating SOM, PCA, and K-means techniques, elucidates hydrochemical types. SOM component maps reveal a close combination of various hydrochemical components. Principal component analysis (PCA) identifies the first principal component (PC1), explaining 48.15% of the variance. The second (PC2) and third (PC3) principal components, explain 13.2% and 10.8% of the variance, respectively. K clustering categorized samples into three main clusters: one less influenced by basaltic geological processes, another showing strong igneous rock weathering characteristics, and the third affected by other geological processes or anthropogenic factors.

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Hydrochemical characteristics and sources of groundwater pollution in Soubré and Gagnoa counties, Côte d’Ivoire

Cited by 3 publications

References 64 publications

Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques

Predicting irrigation water quality indices in a typical mining dominated area in the Upper West region of Ghana using multiple machine learning techniques

Spatial variability, source identification, and partitioning of groundwater constituents in a typical lakeside plain on Yungui Plateau

Analysis of the Hydrogeochemical Characteristics and Origins of Groundwater in the Changbai Mountain Region via Inverse Hydrogeochemical Modeling and Unsupervised Machine Learning

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