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 ($\mathrm{S}\mathrm{A}\mathrm{R},\mathrm{R}\mathrm{S}\mathrm{C},\mathrm{N}\mathrm{a}\mathrm{\%},\mathrm{M}\mathrm{H},\mathrm{a}\mathrm{n}\mathrm{d} \mathrm{P}\mathrm{I}),$ 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.

Ain Sefra is one of the Algerian cities that had been experienced several devastating floods during the past 100 years. The purpose of this study is to simulate runoff in the semi-arid region of Ain Sefra watershed through the employing of the Hydrologic Engineering Center – Hydrologic Modelling System (HEC-HMS). In this paper, the frequency storm is used for the meteorological model, the Soil Conservation Service – curve number (SCS-CN) is selected to calculate the loss rate and Soil Conservation Service unit hydrograph method have been applied to simulate the runoff rate. After calibration and validation, the simulated peak discharges were very close with observed values. The Nash–Sutcliffe efficiency coefficient was 0.95, indicates that the hydrological modeling results are satisfactory and accepted for simulation of rainfall-runoff. The peak discharges obtained for the 10, 50, 100 and 1000 year storms are respectively 425.8, 750.5, 904.3 and 1328.3 m³∙s⁻¹.

Groundwater quality in arid areas is an issue that requires attention, as often the only source of irrigation and drinking. In this manuscript, a research area of 4590.2 Km², which depicts important problems of water scarcity and water-poor quality in an arid environment, was assessed. Therefore, GIS-based mapping of the region was carried out to examine the adequacy of groundwater quality. Forty-three (43) groundwater samples were gathered in the fieldwork in March 2022, and various physicochemical limits were analysed within Gibbs and Piper diagrams. Likewise, spatial distribution maps of twelve (12) main water quality parameters were performed using the Inverse Distance Weighted interpolation method (IDW) in the ArcGIS tool. The Water Quality Index (WQI) has been calculated considering Algerian drinking water quality standards and to know the suitability of water for human consumption. A Pearson’s test was carried out to explore the correlations among the different variables. Major results of the study showed that, among anions and cations, sulphates ( $$SO_{4}^{ - 2}$$S O 4 - 2 ) and calcium (Ca) were the most common ions. The WQI showed that 30.23% of the samples have excellent water quality, 62.79% belong to the good water category, and 6.97% fall into the poor water quality category. Thus, the correlation study between many physicochemical properties also showed significant positive relationships between WQI, TDS, EC, TH, $${\text{SO}}_{4}^{ - 2}$$SO 4 - 2 , $${\text{Ca}}^{ + + }$$Ca + + , and $${\text{Mg}}^{ + + }$$Mg + + . In agreement with the Gibbs diagram, most of the samples fall into the field of rock-water interaction dominance. Hence, this research shows that using GIS and WQI as joint approaches could be useful tools for the decision-making process of local and international authorities and water resources management.

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO₃), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

Irrigation water quality is crucial for sustainable agriculture and environmental health, influencing crop productivity and ecosystem balance globally. This study evaluates the performance of multiple deep learning models in classifying the Irrigation Water Quality Index (IWQI), addressing the challenge of accurate water quality prediction by examining the impact of increasing input complexity, particularly through chemical ions and derived quality indices. The models tested include convolutional neural networks (CNN), CNN-Long Short-Term Memory networks (CNN-LSTM), CNN-bidirectional Long Short-Term Memory networks (CNN-BiLSTM), and CNN-bidirectional Gated Recurrent Unit networks (CNN-BiGRUs). Feature selection via SHapley Additive exPlanations (SHAP) provided insights into individual feature contributions to the model predictions. The objectives were to compare the performance of 16 models and identify the most effective approach for accurate IWQI classification. This study utilized data from 166 wells in Algeria’s Naama region, with 70% of the data for training and 30% for testing. Results indicate that the CNN-BiLSTM model outperformed others, achieving an accuracy of 0.94 and an area under the curve (AUC) of 0.994. While CNN models effectively capture spatial features, they struggle with temporal dependencies—a limitation addressed by LSTM and BiGRU layers, which were further enhanced through bidirectional processing in the CNN-BiLSTM model. Feature importance analysis revealed that the quality index (qi) qi-Na was the most significant predictor in both Model 15 (0.68) and Model 16 (0.67). The quality index qi-EC showed a slight decrease in importance, from 0.19 to 0.18 between the models, while qi-SAR and qi-Cl maintained similar importance levels. Notably, Model 16 included qi-HCO₃ with a minor importance score of 0.02. Overall, these findings underscore the critical role of sodium levels in water quality predictions and suggest areas for enhancing model performance. Despite the computational demands of the CNN-BiLSTM model, the results contribute to the development of robust models for effective water quality management, thereby promoting agricultural sustainability.