Arid regions of Algeria face persistent water crises due to recurrent droughts and growing water demand, especially for drinking water and agriculture. This pressure have led to overexploitation of local aquifers. Accurately identifying potential groundwater recharge zones is therefore critical for sustaining aquifer replenishment and water security. In this study, we assess groundwater recharge potential zones in the Khanguet Sidi Nadji watershed in North-Eastern Algeria, employing a spatial multi-criteria decision-making (MCDM) methodology which is based on the AHP, that integrates remote sensing and Geographic Information Systems (GIS). The work methodology uses essential data from ten (10) factors, including lithology, rainfall, curve number, land use, lineament density, slope, drainage density, peak runoff, and soil hydrogeological group. Thematic maps /layers of each factor are produced using ArcGIS 10.8 and integrated using AHP to generate a composite groundwater recharge potential map. Results indicate that areas with high recharge potential comprise approximately one-third of the watershed, covering 27% of the area, moderate potential covers 41% and low potential covers 32%. We validated the final recharge potential map by comparing it with data from 55 high-yield wells distributed across the watershed. A strong correlation (r = 0.74) was found between high-potential zones and well locations. Overall, the findings of this study provide a powerful decision-making tool that contributes to the improved exploitation and protection of groundwater resources, thereby enhancing sustainable water resources management and assisting in addressing the growing challenges of water scarcity in the arid regions of Algeria.

Urban planning plays a critical role in sustainable city development by guiding urban expansion efficiently. In Algeria, the master plan for development and urban planning (PDAU) designates specific areas for city growth, yet the suitability of these areas for urban extension often remains unassessed using systematic methods. Most PDAU, including the plan for Setif City established in 2016, rely on planning approaches and data that risk becoming outdated due to rapid urban changes and evolving spatial dynamics. As a result, there is a pressing need to critically assess and validate these designated extension zones using updated, objective analytical tools. This study addresses this gap by applying an integrated approach combining Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP) to evaluate land suitability for urban extension in Setif City. Fifteen socio-economic, physical, environmental, and accessibility criteria were applied to assess areas designated in the PDAU. The methodology enables a multi-criteria, data-driven analysis to prioritize zones for sustainable urban growth. About 21.5% of the study area is categorized as very high suitability, and most of these sites are concentrated around the edges of the city. 36.7% is classified as high suitability, according to the suitability analysis for future urban expansion. Moderately and poorly suitable areas make up 23.79% and 13% of the total. Merely 5% of the land is deemed to be extremely unsuitable for the extension. The findings support evidence-based urban planning, offering actionable insights for policymakers and urban planners. This study contributes methodologically by demonstrating the effective integration of AHP with GIS in an Algerian context, encouraging replication and further research in similar urban environments.

This study focuses on assessing the hydrogeochemical characteristics and irrigation suitability of groundwater in the Ras El Aioun and Merouana districts, using an integrated approach that combines physicochemical analysis, machine learning (ML), and Geographic Information Systems (GISs). Thirty groundwater samples were collected in June 2023 and subjected to extensive analyses, including major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃⁻, Cl⁻, SO₄²⁻), pH, TDS, alkalinity, and hardness. Hydrochemical facies analysis revealed that the Ca-HCO₃ type was dominant (93.33%), with some samples exceeding FAO limits, particularly for Na⁺, K⁺, SO₄²⁻, Cl⁻, Mg²⁺, and HCO₃⁻. Assessment of groundwater irrigation suitability revealed generally favorable conditions based on three key parameters: all samples (100%) were classified as excellent based on the Sodium Adsorption Ratio (SAR < 10), 70% showed good-to-permissible status by Sodium Percentage (Na% < 60), and 83.3% were within safe limits for Residual Sodium Carbonate (RSC < 1.25 meq/L). However, the Permeability Index (PI > 75%) categorized 96.7% of samples as unsuitable for long-term irrigation due to potential soil permeability reduction. Additionally, Total Hardness (TH < 75 mg/L) indicated predominantly soft water characteristics (90% of samples), particularly in the central study area, suggesting possible limitations for certain agricultural applications that require mineral-rich water. GIS-based spatial analysis showed that irrigation suitability was higher in the eastern and western regions than in the central zone. Advanced machine learning algorithms provide superior predictive capability for water quality parameters by effectively modeling complex, non-linear feature interactions that conventional statistical approaches frequently fail to capture. Three ML models—Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—were used to predict the Irrigation Water Quality Index (IWQI). XGBoost outperformed the others (RMSE = 2.83, R² = 0.957), followed by RF (RMSE = 3.12, R² = 0.93) and SVR (RMSE = 3.45, R² = 0.92). Integrating ML and GIS improved groundwater quality assessment and provided a robust framework for sustainable irrigation management. These findings provide critical insights for optimizing agricultural water use in water-scarce regions.

Groundwater (GW) quality and contamination by potentially toxic elements (PTEs) are major concerns for environmental sustainability, particularly in arid regions. The aim of this study was to assess the human health risks associated with GW contamination by PTEs in the Terminal Complex (TC) aquifer of the Tolga oasis, located in northeastern Algeria. Seventeen GW samples were analyzed using standard methods to determine contamination levels and associated health risks. Results showed that GW was generally contaminated with lead (Pb), which exceeded the WHO permissible limit of 0.01 mg/L in 76.47 % of the samples. Although some samples were rich in Cr and Mn, their levels were below WHO guidelines. Pollution indices, including Contamination Factor (CF), Heavy Metal Pollution Index (HMI), and Nemerow Pollution Index (NPI), indicated that over 50 % of the samples had medium to high pollution levels. These indices were further estimated using artificial neural network (ANN) and Multiple Linear Regression (MLR) machine learning models, whose performances were validated by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash-Sutcliffe Efficiency Coefficient (NSE). The Taylor diagram analysis showed that MLR models were more accurate than ANN models in estimating GW pollution indices. Mapping these indices using support vector machine (SVM) algorithms and applying chemometric statistical techniques, including principal component analysis (PCA), revealed that alteration of geological formations and anthropogenic activities significantly affected GW contamination by PTEs in the study area. The assessment of health risks associated with heavy metals revealed a significant non-carcinogenic risk, particularly for children, with 41.17 % of samples exceeding the hazard index threshold of 1 due to Pb exposure, while carcinogenic risks were low. This study establishes predictive models based on heavy metal pollution indices, providing crucial information on the spatial distribution of GW contamination. The results support the development of targeted mitigation strategies and intervention plans to safeguard GW resources and public health in the region.

The aquifers in the M’léta Plain are crucial for supplying drinking water and supporting industrial and agricultural water needs. However, they are facing a pollution risk and environmental degradation. The present study aims to assess the groundwater quality in the M’léta Plain, focusing on its physicochemical properties, statistics of the aquifer, pollution risks, and factors influencing the water mineralisation process. The analysis of 16 samples reveals that the water contains high levels of sulphates and chlorides, often accompanied by sodium, calcium, or magnesium. This suggests two distinct water types or facies: one characterised by sodium chloride or calcium chloride, and the other with calcic or sodic sulphate waters, sometimes including magnesium sulphate. These facies may be attributed to the influence of different formations at the outcrop. Statistical analyses reveal a strong correlation between electrical conductivity and the majority of chemical elements, indicating the impact of freshwater interacting with the underlying rock formations on mineralisation. Some results also show undersaturation of certain minerals. Furthermore, the study evaluates the water's suitability for irrigation in the M’léta Plain in accordance with Richards’ classification.

Several hydrologists recently stated that a large portion of the world’s population will face “water stress” in the coming years due to a variety of factors, including global mismanagement of fresh water near coastal plains, which is still being ignored and has become polluted. The alluvial plain of Wadi El Nil is one of these coastal plains, located in one of the wettest regions of Algeria with an annual rainfall rate of around 1000 mm. To quantify and qualify the water potential and to design a water management policy, a study was conducted using a complex of tools, such as geological, geophysical by electrical methods, hydro-climatic, and hydrogeological, to determine the lithology, geometry, and hydrodynamic characteristics of the aquifer.