Water quality study of the foggaras in the Adrar region (southwest Algeria) using WQI and GIS

Nasri, Bahous; Kalloum, Slimane; Benhamza, Messaoud; Taha, Ansari; Benatiallah, Djelloul

doi:10.1007/s12517-022-11050-0

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…At the eastern end of the Touat depression, an intense hydrographic network of small distinct ravines drains the plateau of Tademait. Adrar is mainly an agricultural region characterized by its traditional irrigation system, named "foggara" [35]. Hydrogeologically, the study area is part of the transboundary Northern Saharan Aquifer System (SASS) [36].…”

Section: Study Areamentioning

confidence: 99%

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Derdour

Abdo²,

Almohamad

et al. 2023

Sustainability

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Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO3), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

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Section: Study Areamentioning

confidence: 99%

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Derdour

Abdo²,

Almohamad

et al. 2023

Sustainability

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Quantitative reevaluation of the function of Karez using remote sensing technology

Lou,

Dai,

Yang

et al. 2024

Ecological Indicators

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Geochemical characterization of spring waters from the Tinerkouk region, southwestern Algeria

Bahous,

Benatiallah,

Assia

et al. 2024

Water Practice &Amp; Technology

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The present work focused on the geochemical characterization of spring water in the Tinerkouk region in southwestern Algeria. The suitability of the spring water for drinking was assessed using its physicochemical parameters, which included pH, total dissolved solids, major and minor cation and anion compositions, and electrical conductivity. These parameters were compared to Algerian regulations and WHO (World Health Organization) standards to see if the water met the requirements for both spring water and mineral water according to the prevailing standards in force. For this purpose, 43 spring water samples were collected from Tinerkouk region and especially from mineral water unit of Tazliza, during the period 2009-2022. The results obtained for pH, conductivity, sulfate, chloride, nitrate, sodium, and potassium, showed that the water samples from all the sampled springs were acceptable for consumption and within the acceptable limits in accordance with specified criteria, except for Oudghagh, Fatis, and Benzita. However, at these sites, TDS, conductivity, nitrates, and chlorides exceeded the acceptable values. It was also noted that, the spring water from the Tazliza unit is classified as excellent for drinking purposes and meets the permissible limits defined by the legislation for mineral and spring waters. The results also indicate that the predominant chemical facies of the Tazliza spring waters are chlorinated and sulfated sodium and potassium.

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Water quality study of the foggaras in the Adrar region (southwest Algeria) using WQI and GIS

Cited by 3 publications

References 14 publications

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Prediction of Groundwater Quality Index Using Classification Techniques in Arid Environments

Quantitative reevaluation of the function of Karez using remote sensing technology

Geochemical characterization of spring waters from the Tinerkouk region, southwestern Algeria

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