article · Water
Groundwater quality for agricultural use was evaluated in El Kharga Oasis in Egypt's Western Desert by combining geographic information systems, hydrochemical assessment, and machine learning techniques. Physicochemical parameters were recorded across 140 groundwater wells, revealing that water composition is governed by rock-water interactions, mineral dissolution, and ion exchange processes. Several irrigation water quality indices indicated that the majority of sampled wells are suitable and safe for irrigation, with all samples achieving an excellent rating under the sodium adsorption ratio. To predict eight specific water quality indices, adaptive neuro-fuzzy inference system and support vector machine models were developed. The neuro-fuzzy model demonstrated high predictive accuracy, achieving a testing determination coefficient of 0.97, outperforming the support vector machine at 0.76. These computational models provide dependable frameworks for assessing and managing groundwater resources in arid and semi-arid agricultural regions.
Sustainable farming in arid regions depends on dependable groundwater reserves. Using water with poor mineral composition can cause soil salinisation and degrade crop health. Demonstrating that machine learning models can accurately predict water quality indices allows environmental authorities and agricultural managers to monitor water resources more effectively, helping protect fragile soils and secure food production in water-scarce regions.
This methodology could support digital water-management tools and decision-support software for agricultural planners, irrigation authorities, and environmental monitoring agencies operating in arid regions. Having been applied and tested on field sample data, the approach sits at an applied research stage. Transitioning to commercial or operational use would require packaging the machine learning algorithms into accessible software tools and verifying performance across broader, geographically diverse aquifer systems.
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Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments.
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DOI: 10.3390/w15040694
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