article · Water
An assessment of the Quaternary aquifer in the Al-Jawf Basin, Yemen, evaluated groundwater suitability for agricultural irrigation using multiple analytical methods. Chemical testing established that the relative abundance of cations followed the sequence of calcium, magnesium, sodium, and potassium, whilst anions were dominated by sulphate, followed by chloride, bicarbonate, and nitrate. Various agricultural water evaluation metrics, including the irrigation water quality index, sodium adsorption ratio, and residual sodium carbonate, demonstrated moderate-to-severe usage restrictions in certain groundwater samples. To support resource prediction, an adaptive neuro-fuzzy inference system was trained and tested, predicting irrigation water quality indices with high accuracy. Combining geochemical modelling, geographic information systems, multivariate statistics, and artificial intelligence provides decision-makers with precise data needed to manage local water reserves sustainably.
Reliable groundwater monitoring is critical for sustaining agriculture and safeguarding human health in water-scarce regions. Identifying chemical imbalances and salinity restrictions protects crops from soil degradation and yield loss. Furthermore, deploying predictive computational models allows water managers and regional planners to forecast water quality swiftly, facilitating better resource allocation and long-term environmental stewardship.
The work informs water resource managers, regional planners, and irrigation specialists evaluating groundwater suitability for farming. The integration of artificial intelligence with geochemical indices represents an applied and tested methodology for water quality simulation. While currently structured as an analytical workflow rather than a commercial product, the underlying predictive framework could be adapted into commercial decision-support tools for agricultural monitoring services.
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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.
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DOI: 10.3390/w15081496
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