article · Water
Researchers evaluated groundwater quality for agricultural irrigation in the Zeroud basin using multivariate statistics, geographic information systems, and machine learning models. Physicochemical assessments revealed an ionic abundance dominated by sodium, calcium, sulphate, and bicarbonate, pointing to processes such as water-rock interaction, dolomite dissolution, evaporation, and ion exchange. Standard irrigation indices indicated that while most samples are valuable for agriculture, specific measures like the irrigation water quality index and potential salinity showed severe restrictions. To streamline quality assessments, artificial neural network and extreme gradient boosting regression models were trained on water attributes. The models demonstrated high accuracy, with an extreme gradient boosting framework achieving a test coefficient of determination of 0.913 using three key attributes. These computational tools offer predictive capabilities to help local authorities monitor groundwater and safeguard long-term resource management.
Irrigation requires dependable water quality to prevent soil degradation and protect crop yields. By identifying chemical risks and demonstrating that machine learning can accurately forecast water suitability from a few measurable variables, this approach helps water managers make rapid, data-informed decisions to protect agricultural water supplies without requiring exhaustive, continuous chemical testing.
This research provides applied and tested computational models that could be integrated into software tools for agricultural extension services, irrigation district managers, and environmental monitoring agencies. Because the machine learning models successfully predict irrigation suitability from a small set of attributes, they could reduce testing costs for water authorities, though further software development is required to deploy them as accessible decision-support products.
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In the Zeroud basin, a diverse array of methodologies were employed to assess, simulate, and predict the quality of groundwater intended for irrigation. These methodologies included the irrigation water quality indices (IWQIs); intricate statistical analysis involving multiple variables, supported with GIS techniques; an artificial neural network (ANN) model; and an XGBoost regression model. Extensive physicochemical examinations were performed on groundwater samples to elucidate their compositional attributes. The results showed that the abundance order of ions was Na+ > Ca2+ > Mg2+ > K+ and SO42− > HCO3− > Cl−. The groundwater facies reflected Ca-Mg-SO4, Na-Cl, and mixed Ca-Mg-Cl/SO4 water types. A cluster analysis (CA) and principal component analysis (PCA), along with ionic ratios, detected three different water characteristics. The mechanisms controlling water chemistry revealed water–rock interaction, dolomite dissolution, evaporation, and ion exchange. The assessment of groundwater quality for agriculture with respect IWQIs, such as the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), sodium percentage (Na%), soluble sodium percentage (SSP), potential salinity (PS), and residual sodium carbonate (RSC), revealed that the domination of the water samples was valuable for agriculture. However, the IWQI and PS fell between high-to-severe restrictions and injurious-to-unsatisfactory. The ANN and XGBoost regression models showed robust results for predicting IWQIs. For example, ANN-HyC-9 emerged as the most precise forecasting framework according to its outcomes, as it showcased the most robust link between prime attributes and IWQI. The nine attributes of this model hold immense significance in IWQI prediction. The R2 values for its training and testing data stood at 0.999 (RMSE = 0.375) and 0.823 (RMSE = 3.168), respectively. These findings indicate that XGB-HyC-3 emerged as the most accurate forecasting model, displaying a stronger connection between IWQI and its exceptional characteristics. When predicting IWQI, approximately three of the model’s attributes played a pivotal role. Notably, the model yielded R2 values of 0.999 (RMSE = 0.001) and 0.913 (RMSE = 2.217) for the training and testing datasets, respectively. Overall, these results offer significant details for decision-makers in managing water quality and can support the long-term use of water resources.
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DOI: 10.3390/w15193495
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