article · Water
Evaluating groundwater quality for agriculture is often limited by insufficient sampling budgets in developing regions. Research conducted in Naama, southwest Algeria, combined hydrochemical analysis with machine learning models to assess groundwater suitability for irrigation. Analysis of 166 water samples revealed that over 76 percent of the sources were of excellent or very good quality, while only 4.21 percent were unsuitable for agricultural use. To predict the irrigation water quality index efficiently, three machine learning algorithms were tested: Extreme Gradient Boosting, Support Vector Regression, and K-Nearest Neighbours. The Support Vector Regression model demonstrated strong predictive capability using only four chemical inputs, namely calcium, magnesium, sodium, and potassium, achieving low error rates and high correlation. Similarly, Extreme Gradient Boosting provided high accuracy and stability. These computational methods offer an effective means to support agricultural water management and regional resource allocation despite limited physical sampling data.
Financial constraints in developing regions often restrict the frequency of water sampling needed for safe farming. By demonstrating that machine learning can accurately evaluate irrigation water quality using minimal chemical inputs, this approach reduces laboratory testing costs. It enables regional water authorities and agricultural planners to monitor groundwater resources effectively, ensuring suitable water is allocated for crops while protecting agricultural productivity.
This work enables low-cost decision-support software for agricultural water management, allowing regional water authorities and irrigation managers to assess water quality with reduced chemical testing. The research is applied and tested on regional groundwater datasets, demonstrating high predictive accuracy using just four input parameters. Translating this into practical deployment would require integrating the algorithms into accessible digital monitoring tools or advisory platforms for field practitioners.
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The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.
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DOI: 10.3390/w16020264
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