article · Water
Assessing groundwater suitability for agricultural use is vital in arid regions. This investigation evaluated irrigation water quality in Algeria's Sahara aquifer across the Doucen Plain by examining twenty-seven groundwater samples through standard analytical techniques, geographic information systems, multivariate statistics, and machine learning models. Chemical analyses classified the resource as a calcium-chloride water type shaped by mineral weathering, ion dissolution, and human activity. Evaluated water quality indices showed that one third of the samples faced severe restrictions for irrigation use, whilst the remaining two thirds presented moderate to high restrictions, necessitating careful soil and crop management. To forecast the irrigation water quality index, artificial neural networks and gradient boosting regression were tested. The artificial neural network model achieved superior predictive performance, demonstrating that combining geochemical indices with machine learning provides a practical framework for groundwater evaluation.
In arid regions where groundwater is essential for farming, poor water quality can severely damage crops and soil structure. Demonstrating that machine learning can accurately forecast irrigation water indices helps resource managers identify salinity risks without extensive manual testing, enabling better decisions on which crops can safely be cultivated.
The predictive machine learning approach could inform decision-support software for agricultural extension services, water resource authorities, and irrigation planners managing arid aquifers. The work represents applied research tested on a local dataset of twenty-seven samples. Moving toward commercial software tools or field deployment would require validating the predictive models across larger geographical zones and integrating them into user-facing monitoring platforms.
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In order to evaluate and project the quality of groundwater utilized for irrigation in the Sahara aquifer in Algeria, this research employed irrigation water quality indices (IWQIs), artificial neural network (ANN) models, and Gradient Boosting Regression (GBR), alongside multivariate statistical analysis and a geographic information system (GIS), to assess and forecast the quality of groundwater used for irrigation in the Sahara aquifer in Algeria. Twenty-seven groundwater samples were examined using conventional analytical methods. The obtained physicochemical parameters for the collected groundwater samples showed that Ca2+ > Mg2+ > Na+ > K+, and Cl− > SO42− > HCO3− > NO3−, owing to the predominance of limestone, sandstone, and clay minerals under the effects of human activity, ion dissolution, rock weathering, and exchange processes, which indicate a Ca-Cl water type. For evaluating the quality of irrigation water, the IWQIs values such as irrigation water quality index (IWQI), sodium adsorption ratio (SAR), Kelly index (KI), sodium percentage (Na%), permeability index (PI), and magnesium hazard (MH) showed mean values of 47.17, 1.88, 0.25, 19.96, 41.18, and 27.87, respectively. For instance, the IWQI values revealed that 33% of samples were severely restricted for irrigation, while 67% of samples varied from moderate to high restriction for irrigation, indicating that crops that are moderately to highly hypersensitive to salt should be watered in soft soils without any compressed layers. Two-machine learning models were applied, i.e., the ANN and GBR for IWQI, and the ANN model, which surpassed the GBR model. The findings showed that ANN-2F had the highest correlation between IWQI and exceptional features, making it the most accurate prediction model. For example, this model has two qualities that are critical for the IWQI prediction. The outputs’ R2 values for the training and validation sets are 0.973 (RMSE = 2.492) and 0.958 (RMSE = 2.175), respectively. Finally, the application of physicochemical parameters and water quality indices supported by GIS methods, machine learning, and multivariate modeling is a useful and practical strategy for evaluating the quality and development of groundwater.
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DOI: 10.3390/w15020289
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