Mubarak Ali Alshabibi

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Hatem Saad Ramadan

Beni-Suef University

Mohamed Hamdy Eid

University of Miskolc

Mohammed Hezam Al-Mashreki

Mohamed Gad

University of Sadat City

Ali A. AlHammadi

Khalifa University of Science and Technology

András Székács

Magyar Agrár- és Élettudományi Egyetem

Mohammed Saleh Alrakhami

Péter Szűcs

University of Miskolc

Mosaad Khadr

University of Bisha

Omar Saeed

Montefiore Medical Center

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Beni-Suef University

University of Sadat City

University of Miskolc

Research Centre for Astronomy and Earth Sciences

Magyar Agrár- és Élettudományi Egyetem

Agricultural Research Center

Ain Shams University

Desert Research Center

Ministry of Agriculture and Land Reclamation

Khalifa University of Science and Technology

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Integration of Geochemical Modeling, Multivariate Analysis, and Irrigation Indices for Assessing Groundwater Quality in the Al-Jawf Basin, Yemen

Water (2023)

Mohammed Hezam Al-Mashreki Mohamed Hamdy Eid Omar Saeed András Székács Péter Szűcs

Water quality monitoring is crucial in managing water resources and ensuring their safety for human use and environmental health. In the Al-Jawf Basin, we conducted a study on the Quaternary aquifer, where various techniques were utilized to evaluate, simulate, and predict the groundwater quality (GWQ) for irrigation. These techniques include water quality indices (IWQIs), geochemical modeling, multivariate statistical analysis, geographic information systems (GIS), and adaptive neuro-fuzzy inference systems (ANFIS). Physicochemical analysis was conducted on the collected groundwater samples to determine their composition. The results showed that the order of abundance of ions was Ca2+ > Mg2+ > Na+ > K+ and SO42− > Cl− > HCO3− > NO3−. The assessment of groundwater quality for irrigation based on indices such as Irrigation water quality index (IWQI), sodium adsorption ratio(SAR), sodium percent (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate RSC, which revealed moderate-to-severe restrictions in some samples. The Adaptive Neuro-Fuzzy Inference System (ANFIS) model was then used to predict the IWQIs with high accuracy during both the training and testing phases. Overall, these findings provide valuable information for decision-makers in water quality management and can aid in the sustainable development of water resources.

Sodium adsorption ratio

Sodium carbonate

10.3390/w15081496

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