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.

For shallow groundwater, hydrogeochemical processes and quality assessment must be addressed because shallow groundwater is freely available in many parts of the globe. Due to recent anthropogenic activities and environmental changes in Sakrand, Sindh, Pakistan, the groundwater is extremely vulnerable. To provide safe drinking and agricultural water, hydrogeochemical analysis is required. Ninety-five groundwater samples were analyzed using agricultural and drinking indices to determine the hydrogeochemical parameters using multivariate analysis such as Pearson correlations, principal component cluster analysis, as well as Piper diagrams and Gibbs plot for drinking and agricultural indices. An abundance of ions was observed through the statistical summary; however, cations and anions were recorded in the orders Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻ > F⁻. The hydrogeochemical process used to quantify the major reactions occurring in the groundwater system showed rock dominance; the Piper diagrams evaluated the water type. A mixed pattern of calcium, magnesium, and chloride ions (Ca²⁺−Mg²⁺−Cl⁻ type) was observed. Additionally, the ion exchange method showed an excess of bicarbonate ions due to carbonic acid weathering. The water quality index (WQI) resulted 32.6% of groundwater being unsuitable for human consumption; however, the United States Salinity Laboratory (USSL) diagram showed 60% of samples were unsuitable for irrigation due to high salinity and the Wilcox diagram depicted 5% of samples lying in the unsuitable region. Most of the water samples were suitable for drinking; only a few samples were unsafe for drinking purposes for children due to the high hazard index.

Groundwater quality assessment is crucial for the sustainable management of water resources in arid regions, where groundwater is the primary source of water supply and increasing demand raises concerns. The study area in Southwest Algeria relies heavily on groundwater as a source of water supply, and the increasing demand for freshwater raises concerns about the quality of groundwater. To assess the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region, multivariate statistical methods, geochemical modeling and water quality indices were employed. The study revealed that the groundwater samples could be classified into four water groups using hierarchical cluster analysis Q mode (HCA), namely Ca–Mg–HCO₃, Ca–Mg–Cl–SO₄, Ca–SO₄ and Na–Cl. Factor analysis was used to identify the main factors controlling the study area’s hydrochemical processes. The results indicated that water–rock interaction, reverse ion exchange and anthropogenic pollution were the main hydrochemical processes affecting groundwater chemistry. The water quality index indicated that the groundwater was suitable for human consumption, with only 2.32% of the samples being unsuitable. Additionally, the groundwater was suitable for agricultural use, but salinity control was necessary. The saturation index values showed that the groundwater was supersaturated with aragonite, calcite, dolomite, anhydrite and gypsum, and undersaturated with halite. Ca-smectite, Mg-smectite and kaolinite were identified as the primary processes controlling the chemical composition of groundwater. The application of multivariate statistical methods, geochemical modeling and water quality indices provided a comprehensive understanding of the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region. The findings of the study can serve as a useful basis for future studies on groundwater quality assessment in the region.

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EP_d) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EP_d were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EP_d time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.