AI and Machine Learning-Based Spatial Modeling of Groundwater Quality Indices and Hydrogeochemistry for Accurate Prediction of Seawater Intrusion and Irrigation Sustainability in Coastal Agroecosystems

AI and Machine Learning-Based Spatial Modeling of Groundwater Quality Indices and Hydrogeochemistry for Accurate Prediction of Seawater Intrusion and Irrigation Sustainability in Coastal Agroecosystems | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI and Machine Learning-Based Spatial Modeling of Groundwater Quality Indices and Hydrogeochemistry for Accurate Prediction of Seawater Intrusion and Irrigation Sustainability in Coastal Agroecosystems Hatim Sanad, Rachid Moussadek, Abdelmjid Zouahri, Majda Oueld Lhaj, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7705609/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the quality and spatial variability of groundwater in the coastal agricultural zone of Skhirat, Morocco, under growing environmental and anthropogenic stress. The main objectives were to assess hydrogeochemical characteristics, evaluate groundwater suitability for drinking and irrigation, quantify saltwater intrusion, and model quality indices using artificial intelligence. Groundwater (GW) samples were collected and analyzed for physico-chemical parameters. Hydrogeochemical characterization was performed using Piper, Gibbs, and Chadha diagrams. Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), and Saltwater Mixing Index (SMI) were computed using standard equations. Statistical analyses included correlation matrices, Principal Component Analysis (PCA), and K-means clustering. Machine learning models (Random Forest (RF) and Artificial Neural Networks (ANN)) were applied to predict WQI, IWQI, and SMI, followed by spatial interpolation using GIS approach. Results revealed that WQI values ranged from 31.58 to 139.28, with 40% of samples falling in the "poor" to "very poor" categories. IWQI indicate that 43.3% of samples were classified as "good" and 6.7% as "very poor" for irrigation practices. SMI values >1, indicating seawater intrusion, were observed in 30% of samples. The ANN model achieved high predictive accuracy for IWQI (R²=0.81), while RF performed best for SMI (R²=0.74). Spatial analysis confirmed salinization patterns toward coastal zones. These findings highlight the value of integrated AI and geostatistical approaches for sustainable groundwater monitoring and management in vulnerable coastal aquifers. Environmental Engineering Agrochemicals Groundwater Quality Seawater Intrusion (SWI) Random Forest (RF) Artificial Neural Networks (ANN) Hydrogeochemical Analysis Morocco Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Groundwater (GW) is the backbone of global water supply, providing nearly 60% of all drinking water and accounting for over 40% of irrigation water used in agriculture worldwide (Scanlon et al., 2023 ). Its strategic importance is especially pronounced in coastal zones, where more than 40% of the global population resides within 100 kilometers of the ocean (Wedding et al., 2024 ). These densely populated and economically productive areas are facing increasing stress on GW reserves due to the combined pressures of urbanization, agricultural intensification, and industrial activities. Coastal aquifers are particularly sensitive hydrogeological systems due to their proximity to the ocean and the natural hydraulic gradient that controls the freshwater-saltwater interface (Ismail et al., 2024 ). Under normal conditions, freshwater in coastal aquifers forms a lens over the denser seawater. However, when the rate of GW extraction exceeds natural recharge, common in agricultural zones and urban centers, this delicate equilibrium is disrupted, allowing seawater to intrude inland (Perumal et al., 2024 ). Climate change adds another dimension to this vulnerability, with rising sea levels, reduced precipitation, and increased evapotranspiration exacerbating the risks of aquifer salinization (Tackley et al., 2025 ). Coastal GW quality thus becomes a critical concern, threatening not only human consumption but also food production and ecosystem services in these regions. Seawater intrusion (SWI) is one of the most widespread forms of GW contamination in coastal areas. It occurs when saline water from the ocean encroaches into freshwater aquifers, driven by over-extraction, sea-level rise, and declining recharge rates (Zhang et al., 2025 ). Globally, prominent SWI hotspots have been identified in the Nile Delta (Egypt) and much of the Mediterranean basin (El-Naggar et al., 2025 ). The consequences of SWI include deterioration of drinking water quality, soil salinization, crop failure, and loss of agricultural productivity (Panagos et al., 2025 ). In Morocco, SWI represents a growing concern, particularly in the Gharb, Saïss, and Souss-Massa basins regions where intensive agriculture and expanding urbanization place tremendous stress on GW resources (Sanad et al., 2024d ). Factors contributing to SWI in Morocco include poorly regulated GW abstraction, absence of recharge control infrastructure, and reliance on shallow aquifers for irrigation. The situation is aggravated by declining precipitation trends and increasing evapotranspiration due to higher temperatures, both linked to climate change (Chrif El Idrissi et al., 2025). Consequently, sustainable GW management and early warning systems become essential in mitigating the long-term impacts of SWI on water security and agricultural sustainability in Morocco's vulnerable coastal belts. Intensive agriculture, while essential for food security, is a major driver of GW degradation in many coastal zones (Tefera et al., 2024 ). High dependency on GW for irrigation, coupled with unsustainable fertilization practices, contributes to the mobilization of salts and chemical residues into aquifers (Shah et al., 2025 ). In semi-arid and arid regions, such as Morocco’s Atlantic coast, excessive abstraction of GW for year-round irrigation results in both water table decline and quality deterioration through seawater intrusion (Oueld Lhaj et al., 2024b ; Sanad et al., 2024a ). Moreover, the leaching of nitrates, phosphates, and agrochemicals into GW disrupts its potability and reduces its suitability for irrigation (Malik et al., 2024 ). Salinization due to seawater intrusion has direct consequences for soil fertility. Elevated concentrations of sodium and chloride ions lead to ionic imbalances in the soil matrix, causing dispersion of soil particles, reduced permeability, and lower water infiltration (Okebalama et al., 2024 ; He et al., 2025 ). These changes hinder plant root development and reduce nutrient uptake, significantly affecting crop yields and health (Dai et al., 2025 ; Wang et al., 2025 ). The soil sodicity and salinity can impair microbial activity, degrade organic matter, and lead to irreversible declines in soil structure (Malal et al., 2025 ). In our study region, where avocado, tomato, and other high-value crops dominate agricultural systems, such salinization processes could undermine long-term productivity and sustainability. Over the last decade, artificial intelligence (AI) has emerged as a transformative tool in environmental monitoring, offering powerful techniques for data analysis, pattern recognition, and predictive modeling (Leena Sri et al., 2025 ). In hydrogeology, machine learning algorithms such as Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) have been applied to simulate aquifer behavior, classify water quality, and detect pollution hotspots with high accuracy (Iqbal et al., 2024 ; Saleh and Rasel, 2024 ; Igwebuike et al., 2025 ). These approaches are particularly valuable when dealing with multivariate, nonlinear datasets common in groundwater systems. AI techniques are now being increasingly combined with traditional hydrogeochemical and geostatistical methods to enhance spatial predictions and decision-making (Chen et al., 2024 ). For instance, ANN models can capture complex nonlinear relationships between hydrochemical variables and water quality indices, while ensemble methods like RF can provide robust predictions even in small datasets with mixed variable importance (Isık and Akkan, 2025 ). Integrating AI with GIS-based spatial interpolation tools such as Inverse Distance Weighting (IDW) and Kriging allows for the generation of high-resolution groundwater quality maps, supporting targeted management in sensitive coastal zones (Sanad et al., 2024b ). Despite advances in GW monitoring and AI-based modeling, most studies in Morocco and similar semi-arid regions have focused on either hydrochemical assessment or basic geospatial mapping. Limited research has integrated multiple water quality indices such as the Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), and Saltwater Mixing Index (SMI) with advanced multivariate statistical methods like principal component analysis (PCA) and K-means clustering, as well as machine learning algorithms within a single framework to assess GW degradation due to seawater intrusion and agricultural stress (Gad et al., 2024 ; Silwani et al., 2025 ). This research fills an important gap by offering a comprehensive, data-driven framework for monitoring coastal water quality in the Skhirat region of Morocco. The methodology integrates hydrogeochemical assessment and statistical modeling with geospatial interpolation techniques (IDW), computes key water quality indices, and applies predictive modeling using RF and ANN. The outputs of these AI-based models are further spatially visualized to provide actionable insights into GW quality dynamics and the influence of seawater intrusion and agricultural pressures. The primary objectives of this study are: (1) to characterize the hydrogeochemical properties of GW and investigate their spatial patterns across the study area; (2) to perform advanced statistical analyses including correlation analysis, PCA, and K-means clustering to identify underlying geochemical processes and group similar water types; (3) to evaluate GW suitability for drinking and agricultural irrigation using established indices such as the WQI and IWQI; (4) to delineate and quantify the degree of seawater intrusion by applying both graphical methods (Piper, Gibbs, and Chadha diagrams) and the SMI; (5) to develop predictive models for WQI, IWQI, and SMI using ML techniques, specifically RF and ANN and (6) to generate high-resolution spatial distribution maps of predicted GW quality indices using GIS-based interpolation techniques to support targeted water resource management and mitigation strategies. 2. Materials and Methods 2.1. Sampling Location Description The investigation was carried out in the Skhirat coastal zone, located between Rabat and Casablanca within the Rabat-Salé-Kénitra region of Morocco ( Fig. 1 ) . This area is bounded by the Oued Ykem to the northeast, Oued Cherrat to the south, and the Atlantic Ocean to the west (Zouahri et al., 2015 ). Geographically, it lies around 33°51′13″N and 7°02′08″W. The region is characterized by intensive horticultural activity, relying heavily on shallow groundwater for irrigation. It experiences a mild Mediterranean coastal climate with an average annual temperature of 17°C and annual rainfall varying from 250 to 800 mm. Geologically, the area comprises Paleozoic formations of shales, sandstones, and quartzites overlaid by Miocene and Plio-Quaternary calcareous sandstones and marls, forming permeable aquifers. Hydrogeologically, groundwater occurs within these Neogene deposits and shallow alluvial coastal aquifers, recharged mainly by runoff from the Ykem and Cherrat watersheds. The aquifer is phreatic, unconfined, and vulnerable to seawater intrusion due to its proximity to the Atlantic and overexploitation from agriculture. 2.2. GW sampling protocol and analytical procedures GW quality monitoring was conducted in May 2025 at thirty selected locations to evaluate water suitability for both irrigation and domestic use. Sampling sites were systematically distributed based on topographical context, particularly targeting zones of intensive agriculture and areas in close proximity to the coastline ( Fig. 1 ) . The geographic position of each sampling point was precisely recorded using a Garmin Dakota 20 handheld GPS unit. On-site water quality measurements, including pH, electrical conductivity (EC), total dissolved solids (TDS), and dissolved oxygen (DO), were performed with a Bante 900P portable multiparameter probe to ensure real-time data collection (Sanad et al., 2024a ). Water samples were passed through 0.45 µm membrane filters and collected into 500 mL polyethylene containers, which had been pre-rinsed with both distilled water and sample water to minimize any potential contamination. GW table depth at each site was measured using a piezometric probe with a 200-meter cable. The samples were maintained at approximately 4°C in insulated coolers and transported promptly to the laboratory. All procedures followed standard protocols for water quality sampling and analysis as outlined by (Kaiser, 1958 ). Laboratory analyses were performed in triplicate to guarantee data reliability, and the specific analytical techniques employed are summarized in Table 1 , following the guidelines of (Rodier, 1985 ). Table 1 Laboratory analysis instruments and methods for water quality parameters. Parameters Analytical method Equipment model References Potassium (K + ) Flame photometry Jenway PFP7 model apparatus (Rodier, 1985 ; Sanad et al., 2024a ) Sodium (Na + ) Chlorides (Cl − ) Mohr’s method - Calcium (Ca 2+ ) Complexometric method (EDTA titration) - Magnesium (Mg 2+ ) Total Hardness (TH) Carbonate (CO 3 2− ) Titrimetric - Bicarbonate (HCO 3 − ) Nitrate (NO 3 − ) Distillation VELP SCIENTIFICA, Kjeldahl distillation unit, UDK 129 Ammonium (NH 4 + ) Phosphate (PO 4 3− ) UV-visible spectrophotometry JENWAY 6405 model (880 nm) Sulphate (SO 4 2− ) Nephelometric method JENWAY6405 Model (650 nm) 2.3. Statistical Analyses, Multivariate Techniques and Geospatial Mapping To explore the spatial and hydrochemical behavior of GW in the study area, a combination of statistical and geospatial methods was employed. First, descriptive statistics were computed for all physico-chemical parameters using IBM SPSS v25. This provided a general overview of groundwater quality variability and allowed for initial assessment of potential anomalies. Pearson’s correlation matrix was then constructed to identify the strength and direction of linear associations between key variables, offering insights into common sources or linked geochemical processes. Multivariate statistical analysis included Principal Component Analysis (PCA) and K-means clustering. PCA reduced data dimensionality, highlighting principal factors controlling groundwater chemistry (e.g., seawater intrusion, agricultural leaching), while K-means clustering grouped groundwater samples into hydrochemical classes based on similarity in composition. These machine learning methods were implemented in Python using Scikit-learn and Seaborn libraries for advanced visualization (Bala Dhandayuthapani, 2024 ). For geospatial interpretation IDW interpolation using ArcGIS 10.8 was applied to map the spatial distribution of GW parameters and indices across the region (Sanad et al., 2025b , 2025c ). 2.4. Computation of Water Quality Indices 2.4.1. Evaluation of the appropriateness of GW for domestic use and irrigation practices The WQI was calculated using the weighted arithmetic method, where each parameter was assigned a weight based on its relative importance to human health, following WHO ( 2017 ) standards (WHO, 2017 ; El Hammioui et al., 2025 ). The index was computed using the weighted arithmetic method. The index consolidates multiple physicochemical parameters into a single score. Each parameter was assigned a weight (wi) based on its relative importance, and a quality rating scale (qi) was calculated using following Equations (1) to (4) as presented in Table 2 (Sanad et al., 2024a ; Shu et al., 2025 ). Table 2 GW quality indicators and calculation formula. Parameters Quality rating scale (Qi) Weight (Wi) WQI parameters Equations Eq.No. pH 3 0.0491 “Wi” represents the relative weight of each parameter \(\:\mathbf{W}\mathbf{i}=\frac{\mathbf{w}\mathbf{i}}{{\sum\:}_{\mathbf{i}=\mathbf{n}}^{\mathbf{n}}\mathbf{w}\mathbf{i}}\) (1) EC 5 0.0819 DO 5 0.0819 TDS 3 0.0491 K + 3 0.0491 “Qi” is the quality rating scale “Ci” is the measured concentration of the parameter “Si” standard permissible value (WHO, 2017 ) \(\:\mathbf{Q}\mathbf{i}=\frac{\mathbf{C}\mathbf{i}}{\mathbf{S}\mathbf{i}}\:\times\:100\) (2) Na + 5 0.0819 Cl − 5 0.0819 Ca 2+ 3 0.0491 Mg 2+ 3 0.0491 “SI” sub-index for each parameter \(\:\mathbf{S}\mathbf{I}=\mathbf{Q}\mathbf{i}\:\times\:\mathbf{W}\mathbf{i}\) (3) TH 3 0.0491 HCO 3 − 3 0.0491 NO 3 − 4 0.0655 “WQI” scores \(\:\mathbf{W}\mathbf{Q}\mathbf{I}=\:{\sum\:}_{\mathbf{i}=\mathbf{n}}^{\mathbf{n}}\mathbf{S}\mathbf{I}\) (4) NH 4 + 3 0.0491 PO 4 3− 4 0.0655 SO 4 2− 5 0.0819 Total weight 57 1 WQI values were classified as Excellent (0–50), Good (50–100), Poor (100–200), Very Poor (200–300) and Unsuitable (> 300) (Sanad et al., 2024a ). The IWQI was computed to determine the appropriateness of groundwater for irrigation by integrating a suite of agro-chemical indicators including EC, SAR, RSC, %Na, MAR, PI, RSBC, KI and PS (Sanad et al., 2024a ). All calculations followed standard equations commonly adopted in irrigation water quality studies ( Table 3 ). Table 3 IWQ indices and mathematical formula. Irrigation indices equations Eq. No. \(\:\varvec{S}\varvec{A}\varvec{R}=\frac{{\varvec{N}\varvec{a}}^{+}}{\sqrt{\frac{{(\varvec{C}\varvec{a}}^{2+}+{\varvec{M}\varvec{g}}^{2+})}{2}}}\) (5) \(\:\varvec{R}\varvec{S}\varvec{C}=\:\left[{\varvec{H}\varvec{C}\varvec{O}}_{3}^{-}+\:{\varvec{C}\varvec{O}}_{3}^{2-}\right]-{[\varvec{C}\varvec{a}}^{2+}+{\varvec{M}\varvec{g}}^{2+}]\) (6) \(\:\varvec{\%}\varvec{N}\varvec{a}=\:\frac{\left({\varvec{N}\varvec{a}}^{+}+{\varvec{K}}^{+}\right)\times\:100}{{\varvec{C}\varvec{a}}^{2+}+{\varvec{M}\varvec{g}}^{2+}+\:{\varvec{N}\varvec{a}}^{+}+{\varvec{K}}^{+}}\) (7) \(\:\varvec{M}\varvec{A}\varvec{R}=\frac{{\varvec{M}\varvec{g}}^{2+}\:\times\:100}{\left({\varvec{C}\varvec{a}}^{2+}+\:{\varvec{M}\varvec{g}}^{2+}\right)}\) (8) \(\:\varvec{P}\varvec{I}=\frac{\left(\sqrt{{\varvec{H}\varvec{C}\varvec{O}}_{3}^{-}}+\:{\varvec{N}\varvec{a}}^{+}\right)\times\:100}{{({\varvec{N}\varvec{a}}^{+}+\:\varvec{C}\varvec{a}}^{2+}+{\varvec{M}\varvec{g}}^{2+})}\) (9) \(\:\varvec{R}\varvec{S}\varvec{B}\varvec{C}=\:{\varvec{H}\varvec{C}\varvec{O}}_{3}^{-}-\:{\varvec{C}\varvec{a}}^{2+}\) (10) \(\:\varvec{K}\varvec{R}=\frac{{\varvec{N}\varvec{a}}^{+}}{{\:\varvec{C}\varvec{a}}^{2+}+{\varvec{M}\varvec{g}}^{2+}}\) (11) \(\:\varvec{P}\varvec{S}={\varvec{C}\varvec{l}}^{-}+\:\frac{1}{2}\:\times\:{\varvec{S}\varvec{O}}_{4}^{2-}\) (12) The IWQI classification system grouped samples into excellent ( 300) categories (Sanad et al., 2024a ). 2.4.2. Seawater intrusion assessment using Saltwater Mixing Index (SMI) This index uses a cumulative probability approach to establish regional thresholds for conservative ions (Na⁺, Mg²⁺, Cl⁻, SO₄²⁻) (Chandrajith et al., 2022 ). Thresholds were derived from a reference freshwater group, and the SMI equation was used to calculate mixing levels across all samples. It is calculated as follows: \(\:SMI=a\:\times\:\:\frac{{C}_{{Na}^{+}}}{{T}_{{Na}^{+}}}\) + \(\:b\:\times\:\:\frac{{C}_{{Mg}^{2+}}}{{T}_{{Mg}^{2+}}}\) + \(\:c\:\times\:\:\frac{{C}_{{Cl}^{-}}}{{T}_{{Cl}^{-}}}\) + \(\:d\:\times\:\:\frac{{C}_{{SO}_{4}^{2-}}}{{T}_{{SO}_{4}^{2-}}}\) (13) where (a = 0.31, b = 0.04, c = 0.57, d = 0.08) represent the relative concentration proportion of ions Na + , Mg 2+ , Cl − , and SO 4 2− in seawater respectively (Wurl et al., 2023 ). C represents the concentration (in mg/L) of the ions in the sampled GW. “T” represent the regional freshwater thresholds and were estimated using cumulative probability analysis from non-saline samples in the study area. Samples were categorized as “freshwater dominant” (SMI 1) (Edet, 2017 ). 2.5. Application of Artificial Intelligence (AI) and Machine Learning (ML) Models for Predicting GW Quality Indices In this study, supervised ML models were implemented to predict three GW quality indices (WQI, IWQI, and SMI) using key physicochemical parameters as input features. Two algorithms were employed and compared including Random Forest (RF) and Artificial Neural Network (ANN), both widely used in water quality modeling due to their flexibility and ability to capture nonlinear relationships (Kumar et al., 2024 ; Zhang and You, 2024 ; Acharki et al., 2025 ). 2.5.1. Input Dataset and Preprocessing The input dataset included measured all GW parameters. These were selected as predictors for WQI, IWQI, and SMI. Data were normalized using min–max scaling to ensure consistent learning by the ANN and to prevent bias toward variables with larger scales. 2.5.2. Random Forest and Artificial Neural Network (ANN) Model The RF model, a robust ensemble of decision trees, was applied with optimized parameters including number of trees (n_estimators) and maximum tree depth (Xu et al., 2024 ). The model was trained using an 80:20 train-test split (Dzulhijjah et al., 2025). Model performance was evaluated using standard metrics (Coefficient of determination (R²), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE)) (Grandika et al., 2024 ). ANN modeling was conducted using a feedforward backpropagation network with three hidden layers. The number of neurons was optimized through cross-validation. Activation functions used were ReLU (for hidden layers) and linear (for output layer) (Jahan et al., 2025 ). The model was trained using the Adam optimizer and mean squared error (MSE) loss function (J et al., 2024 ). Early stopping was applied to prevent overfitting. 2.5.3. Model Comparison, Validation, and Spatial Mapping To assess and compare the predictive capabilities of the machine learning models, both the RF and ANN algorithms were evaluated using standard performance metrics, including the R², RMSE, and MAE (Boutahri and Tilioua, 2024 ; Poudel et al., 2024 ). Predicted values of the GW quality indices (WQI, IWQI, and SMI) were compared against observed values using scatter plots of predicted vs. actual data to visualize model performance and assess fit quality across different models. Subsequently, the predicted outputs of the indices were georeferenced and spatially interpolated using the IDW method in ArcGIS 10.8. 3. Results and discussion 3.1. Spatio-Statistical Evaluation of GW Quality Indicators in the Context of Seawater Intrusion and Agricultural Inputs Groundwater pH reflects the acidic or alkaline nature of the water and influences solubility and mobility of ions. In this study, pH values ranged from 6.91 (sample P3) to 7.73 (sample P8), with a mean of 7.33, remaining within WHO's acceptable range ( Table 4 ) . Table 4 Descriptive statistical analysis results of physico-chemical parameters. Parameters WHO 2017 Values Min Max Mean Std Dev CV (%) Skewness Kurtosis pH 6.5–8.5 6.91 7.73 7.33 0.18 2.42 -0.28 0.43 EC (mS/cm) 1000 1.37 6.3 2.62 0.94 35.45 1.92 5.66 TDS (mg/L) 500 850.9 1521 1183.81 176.45 14.65 -0.48 -0,8 DO (mg/L) 5 0.5 3.36 1.42 0.89 62.01 0.6 -0.75 K + (mg/L) 10 0.6 17.3 3.86 3.58 91.27 1.99 4.67 Na + (mg/L) 200 108 750 284.42 141.88 49.04 1.33 2.57 Cl − (mg/L) 250 223.65 1775 643.26 430.15 65.75 1.36 0.87 Ca 2+ (mg/L) 75 52 444 218 83.22 37.53 0.49 0.48 Mg 2+ (mg/L) 50 48 165.6 96.2 27.05 27.64 0.34 0.08 TH (mg/L) 400 100 609.6 314.2 101.58 31.78 0.43 0.97 CO 3 2− (mg/L) - 75 352.5 223.24 59.42 26.17 -0.18 0.45 HCO 3 − (mg/L) 120 152.5 716.75 453.92 120.82 26.17 -0.18 0.45 CaCO 3 (mg/L) - 125 587.5 372.07 99.04 26.17 -0.18 0.45 NO 3 − (mg/L) 50 6.2 111.6 40.3 31.05 75.76 0.85 -0.5 NH 4 + (mg/L) 35 1.8 12.6 5.64 3.37 58.69 0.83 -0.39 PO 4 3− (mg/L) 5 0.42 2.5 1.45 0.72 48.66 0.25 -1.28 SO 4 2− (mg/L) 250 202.64 466 367.07 73.76 19.76 -0.71 -0.71 The spatial map indicates relatively stable pH values across the coastal strip with minor acidification observed in the southeastern zone, possibly linked to localized agricultural acidification or soil-water interactions ( Fig. 2 ) . EC, a proxy for total ion content and salinity, varied significantly from 1.36 to 6.30 mS/cm, with a high CV% of 35.45%, reflecting substantial heterogeneity in salinity levels. High EC zones, as illustrated in the map, are concentrated near the coastal fringe (samples P13, P14, and P21), suggesting direct seawater intrusion. The mean EC (2.62 mS/cm) exceeds the WHO threshold (1 mS/cm) in over 90% of samples, confirming widespread salinization. TDS followed a similar pattern (850.90–1521.00 mg/L), with 100% of samples exceeding the WHO standard of 500 mg/L, again reinforcing the intrusion of saline waters. TDS peaks in the western coastal zone align spatially with EC hot spots and proximity to the Atlantic Ocean. DO, vital for redox processes, exhibited low values (0.50–3.36 mg/L), with a mean of 1.42 mg/L and high variability (CV% = 62.01%). These values reflect oxygen depletion likely due to the biological activity from fertilizers and organic inputs, especially in inland agricultural areas (Ristea et al., 2025 ). The depressed DO levels are consistent with groundwater under reducing conditions, potentially enhancing mobilization of elements like Fe and Mn (Lo Medico et al., 2025 ). Regarding K⁺, which originates from fertilizers and weathering, values varied from 0.6 to 17.3 mg/L, exceeding the WHO limit of 10 mg/L in 17% of samples. The high CV% (91.27%) and spatial concentration of elevated K⁺ near sample P13 and P23 highlight zones impacted by intensive agriculture. Na⁺ ranged widely between 108.12 mg/L and 749.81 mg/L (mean = 278.22 mg/L), with 100% of samples above the WHO limit (200 mg/L). Its strong spatial gradient toward the northern coast (notably P5 and P13) supports seawater intrusion as the primary source. Cl⁻, a conservative tracer of marine origin, revealed alarming levels between 224.11 and 1773.26 mg/L. Over 90% of samples exceed the WHO threshold (250 mg/L). The spatial map indicates maximum concentrations near samples P13, P16, and P24, strongly coinciding with the Atlantic margin. The sharp salinity gradients and elevated Na⁺/Cl⁻ reinforce that seawater intrusion is a major driver of groundwater degradation in the study area (Perumal et al., 2024 ). Ca²⁺ and Mg²⁺ contribute to water hardness and originate from both seawater mixing and carbonate weathering (Rezaei and Hassani, 2018 ). Ca²⁺ ranged from 64 to 340 mg/L, and Mg²⁺ from 48 to 129.6 mg/L, both exceeding WHO limits (75 mg/L for Mg²⁺ and 100 mg/L for Ca²⁺) in 80% of samples. Spatially, elevated Ca²⁺ concentrations appear inland (P16, P24), whereas Mg²⁺ concentrations cluster along the coast (P13, P14), suggesting a mixed influence from marine intrusion and lithological inputs. Total hardness (TH), a cumulative index of Ca²⁺ and Mg²⁺, ranged from 165.6 to 477.6 mg/L with a mean of 288.1 mg/L, placing the water in the “very hard” category in more than 75% of samples. These high values compromise water suitability for irrigation and domestic use, consistent with strong salinization and geological contributions (lotfinasabasl et al., 2025 ). Carbonates and bicarbonates regulate buffering capacity and reflect equilibrium with geological formations. CO₃²⁻ ranged from 123 to 283.5 mg/L and HCO₃⁻ from 260.45 to 630.2 mg/L. CaCO₃ equivalent ranged from 232.5 to 507.5 mg/L. These elevated values indicate substantial carbonate buffering, possibly from weathering of limestone or dolomite units underlying the aquifer. The highest HCO₃⁻ concentrations are observed inland (P2, P29), while lower levels occur near the coast, where seawater dilution overrides carbonate buffering. These patterns suggest that carbonate levels are more influenced by rock–water interactions in the inland zones than by saline sources (Serati et al., 2025 ). Nitrate (NO₃⁻), a mobile indicator of fertilizer use and organic pollution, ranged from 6.23 to 111.56 mg/L, with 33% of samples surpassing the WHO guideline of 50 mg/L. High NO₃⁻ zones (P1, P26, P27) are inland and spatially align with agricultural zones, confirming fertilizer leaching. The average NO₃⁻ value (39.1 mg/L) and a moderate CV% suggest widespread anthropogenic inputs. Ammonium (NH₄⁺), often found under reducing conditions or from manure infiltration, ranged from 0.5 to 5.4 mg/L. Over 60% of samples exceed the typical natural background level of 0.5 mg/L. Peaks are observed in samples P1, P4, and P20, where both agricultural runoff and organic matter degradation are likely contributing factors (Oueld Lhaj et al., 2024b ). Phosphorus (P), usually present at trace levels in natural groundwater, showed concentrations ranging from 0.12 to 2.35 mg/L (mean = 0.87 mg/L). Over 50% of samples exceed the typical limit of 0.1–0.3 mg/L. The highest concentrations (P26, P27) are located in intensive cultivation areas and reflect phosphate fertilizer use. Sulfate (SO₄²⁻) ranged from 202.94 to 465.98 mg/L with a mean of 308.6 mg/L, exceeding the WHO limit (250 mg/L) in 60% of samples. The spatial map highlights SO₄²⁻ enrichment in coastal samples (P13, P14) and central zones (P20), indicating a combined effect of seawater mixing and agrochemical application (Sanad et al., 2024d ). 3.2. Multivariate Correlation Analysis of GW Physico-Chemical Parameters to Elucidate Salinization and Agricultural Impacts 3.2.1. Pearson correlation The correlation analysis provides critical insights into the relationships among the measured groundwater parameters, helping to elucidate shared origins, geochemical interactions, and potential contamination sources ( Fig. 3 ) . A strong and significant correlation is observed between electrical conductivity (EC) and major cations/anions such as Na⁺ (r = 0.85), Cl⁻ (r = 0.84), K⁺ (r = 0.77), and SO₄²⁻ (r = 0.73). These high correlations confirm that EC is primarily controlled by the concentration of dissolved salts, which are typical indicators of saline intrusion in coastal aquifers (Manhou et al., 2024 ). EC also correlates strongly with TDS (r = 0.70), supporting its use as a proxy for salinity. The Na⁺–Cl⁻ correlation (r = 0.61) is also strong and supports the marine origin of salinization, as these ions dominate seawater composition. Additionally, the correlation between Cl⁻ and SO₄²⁻ (r = 0.64) suggests co-mobilization under saline water mixing and potential agricultural runoff containing sulfate-based fertilizers. Ca²⁺ and Mg²⁺, the primary contributors to water hardness, show a very strong positive correlation (r = 0.98), and both are highly correlated with total hardness (TH), with r = 0.98 for Ca²⁺ and r = 0.75 for Mg²⁺. These results indicate a shared geochemical source, likely carbonate and dolomitic rock dissolution, enhanced by cation exchange reactions intensified by saline water encroachment (Brhane and Mekonen, 2024 ). The moderate correlations of TH with EC (r = 0.74) and Cl⁻ (r = 0.69) further support the contribution of ion exchange processes induced by seawater intrusion, which tend to increase Ca²⁺ in groundwater due to exchange with Na⁺. HCO₃⁻ and CO₃²⁻, despite their geochemical interdependence, show only weak correlations with other ions. Their correlation with EC is weak (r = − 0.28 and r = − 0.14, respectively), suggesting that carbonate equilibrium processes are not directly linked to salinity gradients. This may be due to spatial variability in buffering capacity or anthropogenic inputs disrupting the natural carbonate balance (Huang et al., 2025 ). Furthermore, the inverse relationships between HCO₃⁻ and Na⁺, Cl⁻, and SO₄²⁻ indicate that carbonate-buffered inland zones are less impacted by seawater mixing, which typically dilutes bicarbonate concentrations. The strong positive correlation between NO₃⁻ and NH₄⁺ (r = 0.71) and their even stronger association with P (r = 0.92 for NH₄⁺ and r = 0.71 for NO₃⁻) clearly reflect a common source of contamination: agricultural fertilizers and possibly manure application. These three parameters are weakly correlated with EC and Cl⁻, indicating that agricultural impact is spatially and chemically distinct from seawater intrusion. This pattern suggests localized contamination by nitrogenous and phosphate fertilizers in agricultural zones (notably sites P26, P27), consistent with earlier spatial analyses of nutrient distribution. DO is negatively correlated with most other parameters, particularly NH₄⁺ (r = − 0.72), P (r = − 0.67), and TDS (r = − 0.63). This inverse pattern reflects redox-sensitive conditions, where oxygen is consumed during microbial degradation of organic material and ammonification (Perović et al., 2024 ). It indicates zones with low oxygen and high nutrient loading, typical of areas affected by intensive agriculture and organic waste infiltration. Low DO also suggests reduced conditions, which can enhance the mobility of metals and nutrients, degrading groundwater quality (Zeng et al., 2024 ). The pH exhibits weak to moderate negative correlations with most major ions (e.g., Cl⁻ r = − 0.26, Na⁺ r = − 0.30, K⁺ r = − 0.25), indicating mild acidification in zones impacted by seawater intrusion or fertilizer reactions. The weak correlation with EC (r = − 0.49) suggests that pH variations are likely influenced by localized geochemical buffering, organic acids, and not strongly driven by overall salinity (Manhou et al., 2025 ). Notably, pH correlates positively with DO (r = 0.36), suggesting that aerobic environments maintain higher alkalinity, while reducing conditions (often nutrient-loaded) tend to lower pH. 3.2.2. Principal Component Analysis (PCA) The PCA was conducted to reduce the dimensionality of the physico-chemical groundwater dataset and to identify the dominant factors influencing groundwater quality in the Skhirate coastal aquifer ( Fig. 4 ) (Bhushan et al., 2025 ). The first two principal components (PC1 and PC2) accounted for 65.8% of the total variance, with PC1 explaining 44.4% and PC2 21.4%. PC1 was strongly and positively correlated with EC, TDS, Na⁺, Cl⁻, SO₄²⁻, K⁺, Ca²⁺, Mg²⁺, and TH, indicating a clear salinization gradient likely driven by seawater intrusion and geogenic mineralization processes (Yassin et al., 2024 ). This component reflects the contribution of dissolved ionic species that dominate the hydrochemical signature of marine-influenced groundwater. Conversely, PC2 revealed a distinct nutrient pollution and redox-sensitive axis, positively associated with dissolved oxygen (DO) and negatively correlated with NH₄⁺, NO₃⁻, and PO 4 3 . This trend highlights oxygen-depleted zones enriched with reactive nitrogen and phosphorus, typically linked to agricultural runoff, organic matter degradation, and fertilizer leaching (Chamoli et al., 2024 ). The spatial separation of salinity and nutrient variables across the two components suggests the presence of dual contamination sources including geogenic (marine intrusion) and anthropogenic (agricultural inputs). Moreover, the relatively neutral loading of pH and HCO₃⁻ implies their buffering role in transitional geochemical zones. Overall, PCA confirms that both natural hydrogeochemical processes and intensive agricultural practices play pivotal roles in shaping groundwater quality in this vulnerable coastal region. 3.2.3. K-means clustring The application of K-means clustering (k = 3) to the standardized dataset of 17 physico-chemical groundwater parameters successfully classified the 30 samples into three statistically distinct clusters. The clustering was visualized in a two-dimensional principal component space, where the first two principal components (PC1 and PC2) accounted for 65.8% of the total variance in water quality. Each cluster represents a group of groundwater samples with similar hydrochemical profiles, providing insight into the spatial and environmental processes affecting groundwater quality in the study area ( Fig. 5 ) . Cluster 1, predominantly located in the upper left quadrant of the PCA biplot, includes samples characterized by moderate concentrations of salinity-related parameters (Na⁺, Cl⁻, EC, TDS) and relatively balanced nutrient levels. These samples likely represent zones where groundwater is influenced primarily by natural geochemical interactions, such as water-rock interaction and carbonate dissolution, without significant anthropogenic stress (Jodhani et al., 2025 ). The moderate placement along PC1 and minimal loading on PC2 indicate stable oxic conditions and limited agricultural input. Cluster 2 appears clustered in the lower left quadrant of the PCA plane and is clearly separated from the other clusters along PC2. It groups samples that are enriched in NH₄⁺, NO₃⁻, and P, and have relatively low DO concentrations, pointing to intensive agricultural influence. This group reflects groundwater affected by nitrate leaching from fertilizers, infiltration of organic waste, and ammonification under reducing conditions (Oueld Lhaj et al., 2024a ). The low positioning along the PC2 axis confirms suboxic to anoxic conditions within these samples, which are often encountered in intensively cultivated regions with poor drainage and high irrigation inputs. Cluster 3 is mainly located in the right half of the PCA biplot and corresponds to samples with elevated levels of EC, TDS, Na⁺, Cl⁻, Ca²⁺, Mg²⁺, SO₄²⁻, and TH. This cluster represents saline or highly mineralized groundwater, and its alignment along PC1 indicates strong geogenic or marine influence. The location and water quality characteristics suggest seawater intrusion or saline upwelling from deeper aquifers, particularly in wells closer to the Atlantic coast. The chemical signature of this group reflects ion exchange processes and mixing between fresh and saline waters, common in over-exploited coastal aquifers (Kong et al., 2025 ). 3.3. Hydrogeochemical Diagrams for Seawater Intrusion and Salinity Assessment 3.3.1. Hydrochemical Facies Characterization Using Piper Diagram The Piper trilinear diagram provides valuable insights into the hydrochemical facies and dominant geochemical processes affecting the groundwater in the Skhirate coastal aquifer (Somay-Altas and Sanli, 2025 ). As depicted in the diagram, the analysis of the 30 groundwater samples reveals a clear and consistent dominance of the Ca²⁺–Cl⁻ water type, classifying the entire dataset within a single hydrochemical facies ( Fig. 6 ). The anion triangle shows that all groundwater samples cluster strongly towards the chloride apex, indicating a pronounced enrichment in chloride ions across the aquifer. This distinctive distribution is a well-established geochemical signature of saline water intrusion, particularly in coastal settings where the hydraulic gradient is disturbed due to excessive groundwater pumping for irrigation practices. The displacement of fresh water by denser saline water results in chloride enrichment, making Cl⁻ a key tracer in detecting the advancement of seawater inland. On the cation triangle, the majority of the samples do not exhibit a single dominant cation, instead plotting within the “no dominant cation” field, which reflects a mixed Ca²⁺–Mg²⁺–Na⁺ composition. This pattern suggests ongoing cation exchange reactions, likely involving the replacement of Ca²⁺ and Mg²⁺ with Na⁺ in the aquifer matrix. These processes are typically associated with salinity encroachment and long-term geochemical evolution of groundwater. A notable exception is sample P3, which plots distinctly in the magnesium (Mg²⁺) dominant field, indicating localized variations in lithology or water–rock interaction. This sample may reflect areas where dolomitic dissolution or magnesium-rich fertilizers influence groundwater chemistry (Ojo et al., 2025 ). The prevalence of Ca²⁺–Cl⁻ facies across the study area points toward regional-scale saline water mixing, likely linked to the proximity to the Atlantic Ocean and overexploitation of aquifers for irrigation. The uniform chloride dominance coupled with non-specific cation control (except for P3) suggests that diffuse seawater intrusion is a key process, rather than a sharp saline front. The groundwater chemistry is further influenced by ion exchange, rock–water interaction, and possibly return flows from agricultural fields that reintroduce salts and alter ionic ratios (Sanad et al., 2024c ). 3.3.2. Hydrogeochemical Processes Controlling Groundwater Chemistry using Gibbs Diagrams The Gibbs diagrams serve as diagnostic tools to infer the dominant processes that govern groundwater chemistry, precipitation dominance, rock-water interaction, or evaporation–crystallization dominance ( Fig. 7 ) (Zahi et al., 2024 ). In the left diagram, nearly all the groundwater samples fall within the rock dominance zone and cluster between 0.4 and 0.1 on the x-axis, with TDS values ranging from 800 to 1500 mg/L. This positioning suggests that water–rock interaction is the principal mechanism influencing ion concentrations (Zhang et al., 2024 ). The relatively high Cl⁻ proportions may further indicate the possible contribution of marine-derived salts, consistent with coastal seawater intrusion processes. The elevated Cl⁻/HCO₃⁻ ratios support the hypothesis of salinization due to saline water mixing, most likely originating from seawater encroachment. Similarly, the right diagram shows that all samples are also concentrated within the rock dominance field. This indicates cation exchange reactions and dissolution of silicate and carbonate minerals as major controlling factors. The moderate to high relative enrichment of Na⁺ and K⁺ over Ca²⁺ may also reflect anthropogenic inputs, possibly linked to fertilizer use in agricultural activities and ion exchange processes in the aquifer matrix. The alignment of all groundwater samples within the rock dominance field in both diagrams, combined with their relatively high TDS, points to a dual influence of natural geochemical evolution through water–rock interactions and external salinization, likely from seawater intrusion in coastal zones. Additionally, the moderate enrichment in sodium and chloride can also be influenced by agricultural runoff, including fertilizers and irrigation return flow (Manimaran et al., 2025 ). 3.3.3. Chadha Diagram for Salinity Evolution Analysis The Chadha diagram provides a powerful modification of the Piper diagram to simplify the identification of hydrochemical facies and the geochemical evolution processes in groundwater ( Fig. 8 ) (Guettaia et al., 2025 ). Based on the results plotted for the 30 groundwater samples from the Skhirate coastal region, the majority of samples fall into the upper right quadrant (Field 4), which corresponds to a Na–Cl type water. This indicates that groundwater chemistry in the region is strongly influenced by saline water mixing, likely due to seawater intrusion in the coastal aquifer (Thakur et al., 2016 ). Notably, a few samples are scattered into Field 3 (Ca–Cl type), Field 1 (Ca–HCO₃ type) and Field 2 (Na–HCO₃ type), indicating freshwater recharge zones or areas where agricultural return flow or limited cation exchange may still play a role (Wen et al., 2024 ). These points suggest localized areas of reduced salinity impact or hydrochemical buffering due to carbonate weathering. The dominance of calcium and sodium cations and chloride anions, as shown in the prevailing cluster of samples, reinforces the evidence of salinization due to seawater intrusion, especially in the zones closer to the coastline, where electrical conductivity (EC) and TDS levels were already high. This diagram also supports the findings of the Piper and Gibbs plots, confirming that the coastal aquifer is undergoing geochemical transformation from freshwater to saline-dominated facies, influenced by marine intrusion and evaporative concentration, alongside possible anthropogenic inputs from agricultural activities. 3.4. Evaluation of GW Suitability Using WQI, IWQI, and SMI 3.4.1. GW Suitability for Drinking Purposes The Water Quality Index (WQI) serves as a crucial indicator for assessing the suitability of groundwater for human consumption (Barathkumar et al., 2025 ). The analysis of WQI values across the sampled locations reveals considerable spatial variability in groundwater quality. The maximum WQI value was recorded in sample P13, reaching 251.86, which falls within the category of very poor water quality (Fig. 9a) . This high value suggests that the water at this location is highly impacted by contaminants and salinity and is not suitable for drinking without appropriate treatment. In contrast, the lowest WQI was observed in sample P17, with a value of 62.42, classifying it as good water quality. This sample indicates minimal contamination and suggests that the groundwater at this location may be safely used for drinking purposes, with minimal health risks. The mean WQI value across all 30 samples was calculated to be 133.48, placing the average water quality in the poor category. This suggests that, on average, the groundwater in the study area is compromised and does not meet the desirable standards for direct consumption without remediation. The high mean value may be attributed to elevated concentrations of parameters such as TDS, Cl-, Na + and NO3- in several sampling locations, resulting from agricultural runoff, leaching of fertilizers and saline water intrusion. In terms of distribution across WQI categories, the majority of the groundwater samples 76.67% were classified as having poor quality, indicating widespread degradation. Only 20% of the samples were considered to have good quality, and only one sample (P13) fell into the very poor quality category. This distribution underscores a pressing concern regarding the potability of groundwater in the area. 3.4.2. GW quality for irrigation applications The assessment of groundwater quality for irrigation was performed using several indices including SAR, RSC, Na%, MAR, IP, RSBC, Kelly Index (KI), and Potential Salinity (PS) (Sanad et al., 2024a ). These indices help determine the suitability of water for long-term agricultural use and the possible impacts of salinity and sodicity hazards, especially under conditions influenced by seawater intrusion or poor agricultural practices. SAR values ranged from 1.47 (P1) to 7.71 (P13), with a mean of 4.05. Values below 10 are generally considered safe for irrigation ( Table 5 ) . Table 5 Calculated irrigation water quality indices for groundwater samples from the Skhirate coastal region. Samples SAR RSC Na% MAR% IP% RSBC KI PS P1 1.47 -12.38 17.62 36.73 26.83 -7.92 0.21 14.13 P2 1.72 -1.38 24.29 46.40 38.81 0.46 0.32 8.42 P3 1.87 -3.07 25.18 58.49 38.29 1.41 0.33 9.53 P4 6.33 -4.76 52.34 52.12 60.09 -0.54 1.10 13.91 P5 7.67 -12.76 52.81 45.12 57.94 -6.18 1.12 17.46 P6 3.09 5.10 37.19 51.91 52.87 5.16 0.59 12.28 P7 3.28 -3.00 35.74 49.74 46.77 0.27 0.56 13.52 P8 3.16 -8.49 33.00 34.05 41.81 -6.02 0.49 17.27 P9 4.32 -4.36 42.59 47.41 51.98 -1.03 0.74 19.68 P10 5.13 -9.62 44.19 44.18 51.01 -4.58 0.78 33.57 P11 4.60 -11.62 43.22 32.40 49.25 -8.23 0.75 37.45 P12 3.93 -17.66 36.10 37.15 41.38 -11.12 0.56 31.41 P13 7.71 -26.91 48.03 38.08 51.14 -16.61 0.91 53.92 P14 4.94 -4.21 46.75 49.74 55.77 -0.69 0.88 19.38 P15 5.43 1.19 51.89 38.81 63.02 0.91 1.08 17.66 P16 4.01 -15.84 36.39 26.19 42.32 -12.71 0.57 54.34 P17 4.45 -4.30 40.19 42.98 49.18 -1.48 0.67 21.22 P18 4.31 -6.83 42.97 31.54 51.08 -5.28 0.74 19.81 P19 5.09 0.75 50.90 37.46 62.32 0.46 1.03 22.79 P20 2.91 -2.55 45.11 60.35 57.96 -0.10 0.80 8.86 P21 4.54 -6.76 40.00 47.63 48.06 -1.83 0.66 22.42 P22 4.86 -8.91 43.61 42.88 50.77 -4.48 0.76 29.44 P23 4.85 -7.85 43.08 41.25 50.58 -4.12 0.75 38.63 P24 3.82 -16.63 35.63 48.10 41.27 -7.83 0.55 42.43 P25 3.95 0.83 48.62 53.07 62.61 1.91 0.94 12.93 P26 1.50 -11.87 17.93 32.55 27.14 -8.62 0.22 14.42 P27 1.65 -3.68 21.84 43.22 34.46 -1.25 0.27 12.35 P28 3.45 -1.08 40.53 41.28 52.97 -0.19 0.68 11.10 P29 3.34 -2.75 37.38 46.91 48.64 -0.23 0.60 13.23 P30 3.20 -5.36 34.30 40.80 44.36 -2.73 0.52 15.46 Irrigation indices classification Interpretation Range Excellent < 10 - < 20 - - - - < 3 Good 10–18 < 1.25 20–40 75% - - 3–5 Permissible - - 40–60 - 25% – 75% < 2.5 26 > 2.5 > 80 > 50% 2.5 > 1 > 5 The majority of samples fall within the acceptable range, although elevated SAR values, particularly in P13 may indicate potential for soil permeability issues under long-term irrigation (Oueld Lhaj et al., 2025a ). The USSL diagram is an important hydrochemical tool used to evaluate the suitability of GW for irrigation by plotting SAR against EC ( Fig. 10 a ) . These two factors are critical for assessing irrigation water quality as they influence soil permeability, structure, and crop productivity. In this study, all samples fall within the S1 class (low sodium hazard), indicating that the water is safe with respect to sodicity. However, variations in EC place the samples in different salinity hazard categories. The C3–S1 (High salinity, low sodium) class includes the majority of samples (P1, P2, P3, P4, P6, P7, P19, P20, P25, P26, P27, P28, P29, and P30). While sodicity is not a problem, high salinity may pose risks to salt-sensitive crops. It requires moderate leaching and well-drained soils to avoid soil degradation. The C4–S1 (Very high salinity, low sodium) category encompasses P8, P9, P10, P11, P12, P14, P15, P16, P17, P18, P21, P22, P23, and P24. These waters present very high salinity hazards, which could severely affect crop yields and soil quality unless significant leaching is practiced or salt-tolerant crops are cultivated. The agricultural utility of these waters is limited without adequate soil and crop management. Only P13 falls into the extreme class C5–S1 (Extremely high salinity, low sodium). The groundwater at this location is unsuitable for irrigation under normal conditions due to the exceptionally high salinity. It may cause irreversible damage to soil structure and hinder plant growth without advanced irrigation techniques, such as drip irrigation with periodic flushing or blending with fresher water sources. Overall, while sodicity is not a concern for the groundwater samples analyzed, salinity is a significant issue for many samples. The dominance of C3–S1 and C4–S1 classes indicates the influence of natural mineral dissolution, evaporative concentration and seawater intrusion in shaping the groundwater chemistry. Sustainable irrigation practices and monitoring of soil salinity levels are recommended for long-term agricultural productivity in this region. The RSC exhibited a wide variation, from − 26.91 (P13) to 5.10 (P6), with a mean of -6.89. Negative RSC values suggest no hazard from carbonate and bicarbonate accumulation. However, a few positive values near or above 2.5 may indicate moderate to severe hazard, especially for P6. The Na%, a key indicator of sodicity, ranged between 17.62% (P1) and 52.81% (P5), with an average of 38.75%. Na% values above 40% are considered marginal to unsuitable, pointing to potential concerns with sodic water, especially in P5 and P4. This trend may suggest mixing with saline or seawater-affected sources. The Wilcox diagram, which plots Sodium Percentage (%Na) against Electrical Conductivity (EC), is a widely used tool to assess the suitability of groundwater for irrigation based on salinity and sodium hazard (Fentahun et al., 2023 ; Bhushan et al., 2025 ). It provides insight into how dissolved salts and sodium ions might affect soil structure, permeability, and crop yield. Based on the classification results from the Wilcox diagram for the 30 groundwater samples in the Skhirate coastal aquifer, three major water quality groups emerged ( Fig. 10 b ) . These samples (P5, P10, P11, P12, P13, P16, P17, P21, P22, P23, and P24) exhibit high EC values combined with elevated %Na, placing them in the “doubtful” category. This classification implies that the use of such water for irrigation poses significant risks to soil permeability and crop health, especially for sodium-sensitive crops. The high salinity is likely a direct consequence of seawater intrusion, particularly in coastal zones. Additionally, the elevated %Na may be linked to intensive agricultural return flows, fertilizer overuse, and poor irrigation management. Long-term use of this water without remediation may result in sodification of the soil, reducing soil infiltration capacity and damaging soil structure. The samples (P1, P2, P3, P6, P20, P26, and P27) are situated in the “good to permissible” category, indicating that the groundwater is generally suitable for irrigation with minimal salinity or sodium hazard (Somay-Altas and Sanli, 2025 ). The relatively moderate EC and %Na values suggest lower influence from seawater intrusion and agricultural pollution, likely due to natural recharge or better aquifer protection in these areas. These zones might be prioritized for sustainable irrigation practices and groundwater conservation. This group of samples (P4, P7, P8, P9, P14, P15, P18, P19, P25, P28, P29, and P30) exhibits intermediate water quality, often representing transitional zones in the aquifer. The water may be permissible for irrigation but requires monitoring and soil management strategies, particularly for long-term agricultural use. These values may reflect a moderate degree of saline water intrusion or partial anthropogenic contamination. Best management practices (BMPs), such as leaching, crop rotation, and use of gypsum, may help mitigate potential negative effects in these areas (Nthebere et al., 2023 ; Sanad et al., 2025c ). The MAR values spanned from 26.19% (P16) to 60.35% (P20), averaging 43.28%. MAR values > 50% can impair soil structure. A considerable number of samples (e.g., P20, P3, P15) exceeded this threshold, reinforcing concerns regarding long-term soil degradation due to excess magnesium ions.The PI showed values from 26.83% (P1) to 63.02% (P15), with a mean of 48.14%. While most samples fall in the moderate to good category, the variability indicates inconsistent water–soil compatibility, likely related to varying salt compositions across the region. The RSBC values varied from − 16.61 (P13) to 5.16 (P6), with an average of -3.44. Samples with RSBC above 2.5 can negatively affect soil permeability. Again, P6 exhibits the highest RSBC value, suggesting a salinity and bicarbonate hazard. The KI ranged from 0.21 (P1) to 1.12 (P5). Values greater than 1 indicate unsuitability for irrigation. P5 stands out with a KI above the threshold, suggesting high sodium content relative to calcium and magnesium. The PS, which accounts for Cl⁻ and SO₄²⁻ dominance, ranged from 8.42 (P2) to 54.34 (P16), with a mean of 22.56. Higher PS values can indicate salinization risks, particularly under evapotranspiration conditions. The elevated PS in P16 and P13 likely reflects saline intrusion or concentrated agricultural runoff. In summary, while several samples (e.g., P1, P2) demonstrate acceptable irrigation quality across most indices, others (notably P5, P6, P13, P16, and P20) exhibit elevated values in one or more indices that suggest potential degradation due to seawater intrusion, bicarbonate imbalance, and intensive fertilizer use. These results emphasize the need for integrated water management strategies and regular monitoring to mitigate soil salinization risks in coastal and agriculturally active areas. The IWQI values for the 30 groundwater samples in the Skhirate coastal region reflect diverse levels of irrigation suitability, shaped by both seawater intrusion and intensive agricultural activities. The IWQI values ranged from 53.47 in sample P1 to 219.62 in sample P13, with a mean value of 114.37 (Fig. 9b) . The lowest value, observed in P1, reflects good irrigation water quality, indicative of minimal salinity and sodium hazard. On the other hand, the highest value in P13 suggests very poor irrigation suitability, likely resulting from excessive levels of salinity-related parameters such as EC, Na⁺, and Cl⁻. Notably, sample P16 also recorded an IWQI above 200, categorizing it as very poor, and supporting evidence of salinization from seawater intrusion. Based on IWQI classification thresholds, 13 samples (43.33%) fall within the range 50–100, indicating good quality water for irrigation. While 15 samples (50%) fall between 100 and 200, corresponding to poor irrigation quality and 2 samples (P13 and P16) have values between 200–300, classifying them as very poor and unsuitable for irrigation. This classification demonstrates that although a notable proportion of groundwater is suitable for irrigation, over half the samples exhibit limitations, with the poor and very poor classes linked to salt accumulation and high sodium content. These conditions are exacerbated by coastal proximity, where seawater intrusion, coupled with agricultural runoff, likely contributes to deteriorating water quality. 3.4.3. Assessment of Seawater Intrusion Using the SMI The SMI serves as a powerful diagnostic tool for evaluating the degree of seawater intrusion in coastal aquifers (Goswami and Rai, 2024 ). By integrating the relative concentrations of four conservative ions Na⁺, Mg²⁺, Cl⁻, and SO₄²⁻, the SMI provides a dimensionless index that reflects the chemical mixing of marine and freshwater systems. In this study, SMI values ranged from a minimum of 0.53 (sample P1) to a maximum of 3.52 (sample P13), with a mean value of 1.37 (Fig. 9c) . The lowest SMI value, observed in P1, suggests minimal influence from seawater, indicating a more preserved hydrochemical composition of inland or better-protected groundwater. In contrast, P13 exhibits the highest SMI, strongly pointing to intense mixing with saline water, likely due to proximity to the coastline, over-pumping of aquifers, or lateral seawater intrusion. The high mean SMI value (> 1) further emphasizes the systemic vulnerability of the aquifer to salinization processes. The classification of samples based on the SMI threshold of 1 reveals, 12 samples (40%) have SMI 1.0, signifying significant salinity levels attributable to seawater intrusion. The majority of affected samples are geographically concentrated closer to the coast or in areas of intensive irrigation, where high extraction rates may induce seawater ingress. Moreover, the elevated concentrations of Na⁺ and Cl⁻ in these samples corroborate the dominance of marine-derived salinity (Liu et al., 2025 ). These findings align with previous geochemical assessments and support the notion that both natural and anthropogenic forces, particularly unregulated pumping and fertilizer leaching contribute to the deterioration of groundwater quality. 3.5. Integration of AI and ML Models for Predicting Groundwater Quality Indices To enhance the understanding of groundwater quality dynamics and facilitate spatially informed decision-making, ML techniques were applied to predict three key groundwater quality indices including WQI, IWQI and SMI. Random Forest (RF) and Artificial Neural Networks (ANN) were employed for predictive modeling (Philip and Nidhi, 2024 ). These models were trained using comprehensive physico-chemical parameters of groundwater samples collected from the coastal region of Skhirate, Morocco. 3.5.1. Prediction of Water Quality Indices (WQI, IWQI and SMI) The Random Forest model achieved a moderate predictive performance for WQI with an R² value of 0.47, RMSE of 18.87, and MAE of 17.88 ( Fig. 11 a ) . The ANN model showed lower predictive ability for WQI, with R² = 0.29, indicating that Random Forest was better suited for WQI modeling in this study area. The predicted vs. actual scatter plot for the RF model revealed acceptable clustering along the diagonal line, suggesting adequate model fitting despite limited sample size (n = 30) ( Fig. 11 b ) . For IWQI, the ANN model significantly outperformed Random Forest. ANN achieved a high R² value of 0.81, with RMSE = 18.04 and MAE = 14.03, compared to RF’s R² = 0.55. This suggests that ANN effectively captured complex nonlinear relationships between input parameters and IWQI, highlighting its suitability for irrigation quality assessment using integrated physico-chemical datasets. In contrast, the Random Forest model outperformed ANN in predicting SMI. RF attained a high R² score of 0.74, with RMSE = 0.39 and MAE = 0.26. Meanwhile, ANN achieved R² = 0.50, indicating moderate performance. These results reflect that the RF model more effectively captures the drivers of saltwater intrusion through multiple geochemical indicators. Overall, the results affirm the utility of ML models in hydrogeochemical studies for predictive purposes. Random Forest demonstrated superior predictive capacity for WQI and SMI, while ANN excelled in IWQI modeling. The adoption of ML enhances traditional hydrochemical assessments by facilitating early detection of deteriorating water quality and supporting decision-making processes for sustainable groundwater management (Rajeev et al., 2025 ). 3.5.2. Spatial Prediction and Distribution of Groundwater Quality Indices Using AI Models The spatial distribution maps of predicted WQI, IWQI, and SMI were generated using machine learning-based predictions and georeferenced using ArcGIS software. These maps provide critical insights into the spatial variability of groundwater quality across the study area ( Fig. 12 ) . The spatial pattern of predicted WQI values, ranging from 53.78 to 219.96, reveals a pronounced zonal gradient. The lowest WQI values were recorded in the southeastern part of the study area (samples P1 to P3), indicating better water quality for general use. In contrast, the highest WQI values were observed in the northwestern to central-western regions, particularly near sampling points P11, P12, P13, and P16. This cluster of elevated WQI may be linked to anthropogenic influences such as intensive agriculture, wastewater recharge, or seawater mixing (Sanad et al., 2024a ). The northwestern proximity to the coast further supports the influence of marine intrusion and surface runoff on groundwater degradation. The predicted IWQI values varied between 83.27 and 219.16, with a spatial pattern that closely mirrors that of WQI. Higher IWQI values, indicating poorer irrigation suitability, are again concentrated in the northwestern and central zones, corresponding to samples such as P11, P12, P23, and P24. These areas likely face elevated salinity or ion imbalances that are detrimental to sensitive crops. Conversely, the lowest IWQI values indicative of relatively suitable irrigation water, were predominantly located in the southeastern zone (samples P1 to P3), where minimal salt accumulation and geochemical contamination are evident. The spatial interpolation of SMI predictions range between 0.59 and 3.12 offers a more direct representation of seawater intrusion potential. The highest SMI values, pointing to greater influence of marine water mixing, were primarily observed in the central region, notably around sampling points P20 and P25. In contrast, the eastern and southeastern zones, such as around P1 to P3 and P29 to P30, displayed the lowest SMI values, reinforcing the interpretation that these zones are less affected by seawater intrusion. This pattern aligns well with the hydrogeological gradient and topographical drainage trends that may impede inland saltwater migration in those regions. These spatial insights are vital for sustainable groundwater management. The northwestern and central areas, consistently demonstrating high values for all three indices (WQI, IWQI, SMI), are deemed zones of concern requiring urgent monitoring and mitigation interventions. Meanwhile, the southeastern groundwater zones maintain better quality, suitable for both domestic and farming use. The successful integration of AI predictions and GIS-based spatial analysis confirms the value of intelligent modeling approaches in hydro-environmental assessments and supports future predictive and planning frameworks. 3.6. Groundwater Salinization Impacts on Soil Health and Crop Productivity: Soil–Water–Plant Interactions in Coastal Agroecosystems The rise of GW salinity, particularly in coastal agricultural regions such as Skhirat, presents a profound risk to soil functionality and crop performance. Our findings, supported by WQI, IWQI, and SMI indices, reveal that more than 50% of the sampled wells exhibit poor to very poor irrigation suitability, with SMI > 1 in 60% of samples, indicating active seawater intrusion processes. Such GW degradation not only affects water availability but also disrupts the delicate soil–plant–water continuum, leading to cascading agronomic consequences. In saline conditions, osmotic stress is the primary physiological constraint that plants encounter (Zafar et al., 2024 ). High concentrations of Na⁺, Cl⁻ and other salts in irrigation water lower the soil water potential, reducing the ability of roots to absorb water, even when soil moisture appears adequate (Shqerat and Al-Tabbal, 2025 ). This “physiological drought” effect leads to stunted growth, reduced leaf expansion, and impaired stomatal regulation, ultimately lowering photosynthetic efficiency (Du et al., 2024 ). Moreover, ion-specific toxicity especially from Na⁺ and Cl⁻ disrupts intracellular ionic homeostasis. Excess Na⁺ interferes with K⁺ uptake, a critical nutrient for enzyme activation and stomatal functioning, while Cl⁻ accumulation can lead to leaf chlorosis and necrosis, particularly in sensitive crops such as tomato, citrus, and lettuce (Kharwar et al., 2025 ). As demonstrated in our study, high SAR values (> 10 in multiple wells) suggest strong sodicity risks, which impair plant nutrient uptake and water transport mechanisms. From a soil perspective, the prolonged use of saline GW triggers profound changes in soil structure and microbial dynamics. Elevated sodium levels lead to cation exchange imbalances, displacing Ca²⁺ and Mg²⁺ from the soil complex (Regasa et al., 2025 ). This results in soil dispersion, collapse of macro-aggregates, and decreased porosity ultimately reducing infiltration rates, aeration, and root penetration (Mondal et al., 2024 ). These structural changes are especially pronounced in fine-textured alluvial soils, which dominate the Skhirat coastal plain. The microbial ecosystem in saline-affected soils is also altered. High salt concentrations reduce microbial biomass, affect enzyme activities (e.g., dehydrogenase, phosphatase), and shift microbial community composition toward more salt-tolerant taxa (Manhou et al., 2024 ; Sanad et al., 2025a ). This decline in microbial diversity and activity hampers essential soil processes, such as organic matter mineralization and nitrogen cycling, thereby impairing long-term soil fertility. The interaction between saline irrigation and shallow water tables further compounds the problem. In regions where evapotranspiration exceeds precipitation, upward capillary movement brings dissolved salts to the root zone and soil surface, leading to secondary salinization. Crops exposed to this dual threat saline water and saline soils experience compounded physiological stress, especially during germination and early vegetative stages. Furthermore, yield reductions in saline environments can range between 10–50% depending on crop species, soil texture, and irrigation management (El-Ramady et al., 2024 ). These yield penalties are associated not only with reduced water uptake but also with nutrient imbalances, including deficiencies in nitrogen, phosphorus, and potassium, further limiting plant metabolism and reproductive success. Therefore, the combined evidence from salinity indices, hydrogeochemical diagrams, and spatial AI-based predictions confirms a pressing need to monitor and manage GW quality in coastal agricultural systems. The intersection of degraded water quality, fragile soil health, and sensitive crop systems demands a multidimensional strategy that integrates soil–water–plant feedbacks, adapts crop choices, and deploys technological tools for sustainability under evolving climatic and anthropogenic pressures (Djibril et al., 2024 ; Tiabou et al., 2024b , 2024a ; Suh et al., 2025 ; Tiabou et al., 2025 ). 3.7. Recommendations and Mitigation Strategies for Coastal Agroecosystems To mitigate the adverse impacts of GW salinization on agricultural sustainability, a suite of integrated strategies must be adopted at the watershed and farm scale. First, improving irrigation efficiency through technologies such as drip and subsurface irrigation can reduce water loss, minimize salt accumulation, and limit groundwater overexploitation. Coupled with soil moisture sensors and scheduling systems, precision irrigation can optimize water use while maintaining crop yield (Seyar and Ahamed, 2024 ). Second, conjunctive use of surface and GW resources, including the blending of freshwater with moderately saline GW, can help dilute salinity levels, especially during critical crop growth stages. In areas where freshwater is scarce, treated wastewater reuse offers an alternative source of irrigation, provided it meets quality standards. Third, the cultivation of salt-tolerant crops and cultivars (e.g., quinoa, barley, and halophyte vegetables) can maintain farm productivity under increasing salinity stress (Bazihizina et al., 2024 ). These crops exhibit physiological traits such as osmotic adjustment and selective ion transport, allowing them to survive and yield in saline environments. Additionally, organic soil amendments, such as compost, biochar, and green manure, enhance soil buffering capacity, improve structure, and promote microbial activity, all of which are crucial for rehabilitating salt-affected soils (Oueld Lhaj et al., 2024a , 2025b, 2025a ). At the aquifer scale, managed aquifer recharge (MAR) using excess runoff or treated wastewater can dilute saline zones and restore hydraulic balance (Achilleos et al., 2025 ). Monitoring systems supported by AI-based early warning tools should be established to detect salinity thresholds in real-time, enabling proactive management (Miller et al., 2025 ). Finally, strengthening water governance, enforcing extraction quotas, and raising farmer awareness through extension services will be critical to achieving sustainable groundwater management in coastal zones threatened by seawater intrusion. 4. Conclusion This study integrated hydrochemical analysis and artificial intelligence-based modeling to evaluate groundwater quality in a coastal agricultural zone under increasing salinization pressure. By calculating and mapping key water quality indices namely the WQI, IWQI and SMI, the research provided a comprehensive spatial assessment of groundwater conditions using RF, ANN and GIS-based interpolation. The results revealed significant spatial variability in groundwater quality, with pronounced degradation toward the western and northwestern zones, adjacent to the Atlantic coast. These areas showed elevated WQI and SMI values, highlighting the influence of seawater intrusion and unsustainable agricultural practices. The predictive models effectively captured these spatial trends, reinforcing the utility of AI in anticipating groundwater degradation. Seawater intrusion not only increases salinity levels but also disrupts the geochemical balance of soils. In coastal agroecosystems, this leads to reduced soil permeability, impaired nutrient uptake, and ultimately, a decline in crop productivity. Elevated sodium concentrations, evident in SAR and %Na values contribute to clay swelling, soil structure deterioration, and poor infiltration. The progressive salinization trend observed in this study aligns with the regional hydrogeological gradient and may be exacerbated by over-extraction and ineffective irrigation management. To mitigate the risks of groundwater salinization and safeguard agricultural productivity, several strategies are recommended. These include adopting precision irrigation techniques such as drip irrigation to limit salt accumulation, blending freshwater with brackish sources for optimized irrigation, and cultivating salt-tolerant crops like quinoa and barley. Furthermore, managed aquifer recharge (MAR) and AI-assisted early warning systems should be integrated into regional groundwater monitoring frameworks to ensure sustainable resource management in coastal areas. Declarations CRediT authorship contribution statement Hatim Sanad: Conceptualization, Methodology, Software, Resources, Validation, Formal analysis, Writing—original draft, Writing – review and editing, Visualization. Rachid Moussadek: Formal analysis, Writing – review and editing, Funding acquisition. Abdelmjid Zouahri: Methodology, Validation, Writing—original draft, Writing – review and editing, Supervision. Majda Oueld Lhaj: Conceptualization, Methodology, Writing—original draft, Writing – review and editing. Latifa Mouhir: Validation, Writing—original draft, Writing – review and editing, Supervision. Houria Dakak: Validation, Writing—original draft, Writing – review and editing, supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability Data will be made available on request. Acknowledgments The authors are grateful to all collaborators who participated in field sampling, laboratory analyses, and manuscript preparation. The authors are also grateful to the MCGP INRA-ICARDA and EiA projrots for their financial support. References Acharki, S., Raza, A., Vishwakarma, D.K., Amharref, M., Bernoussi, A.S., Singh, S.K., Al-Ansari, N., Dewidar, A.Z., Al-Othman, A.A., Mattar, M.A., 2025. Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates. Scientific Reports 15, 2542. Achilleos, M., Tzoraki, O., Akylas, E., 2025. Managed Aquifer Recharge (MAR) in Semiarid Regions: Water Quality Evaluation and Dynamics from the Akrotiri MAR System, Cyprus. Hydrology 12. https://doi.org/10.3390/hydrology12050123 Bala Dhandayuthapani, V., 2024. Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming. International Journal of Information Technology and Computer Science 16, 72–92. Barathkumar, S., Sellamuthu, K.M., Sathiyabama, K., Malathi, P., Kumaraperumal, R., Devagi, P., Pazhanivelan, S., 2025. Groundwater quality assessment in selected polluted hotspots of Tamil Nadu, India using geospatial and statistical approaches. Desalination and Water Treatment 322, 101047. https://doi.org/10.1016/j.dwt.2025.101047 Bazihizina, N., Papenbrock, J., Aronsson, H., Ben Hamed, K., Elmaz, Ö., Dafku, Z., Custódio, L., Rodrigues, M.J., Atzori, G., Negacz, K., 2024. The Sustainable Use of Halophytes in Salt-Affected Land: State-of-the-Art and Next Steps in a Saltier World. Plants 13. https://doi.org/10.3390/plants13162322 Bhushan, C., Patel, A.P., Kumar, A., Pandey, V.K., Kumar, P.V., Kumar, P., Tiwari, A.K., 2025. Hydrogeochemical characterisation and human health risk assessment of groundwater in Sultanpur District, Uttar Pradesh, India. Frontiers in Water Volume 7-2025. https://doi.org/10.3389/frwa.2025.1639708 Boutahri, Y., Tilioua, A., 2024. Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings. Results in Engineering 22, 102148. https://doi.org/10.1016/j.rineng.2024.102148 Brhane, G.K., Mekonen, H.S., 2024. Identifying controlling factors for the evolution of shallow groundwater chemistry of Ellala catchment, northern Ethiopia. Sustainable Water Resources Management 10, 62. Chamoli, A., Bhambri, A., Karn, S.K., Raj, V., 2024. Ammonia, nitrite transformations and their fixation by different biological and chemical agents. Chemistry and Ecology 40, 166–199. https://doi.org/10.1080/02757540.2023.2300780 Chandrajith, R., Bandara, U.G.C., Diyabalanage, S., Senaratne, S., Barth, J.A.C., 2022. Application of Water Quality Index as a vulnerability indicator to determine seawater intrusion in unconsolidated sedimentary aquifers in a tropical coastal region of Sri Lanka. Groundwater for Sustainable Development 19, 100831. https://doi.org/10.1016/j.gsd.2022.100831 Chen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., Que, X., Li, C., 2024. Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction. Earth Science Informatics 17, 1819–1835. Chrif El Idrissi, M., Saber, E.-R., Al Mashoudi, A., 2025. Hydro-climatic stress and ecosystem vulnerability in a highland lake: the case of Dayet Aoua (1935–2022), Middle Atlas, Morocco. Modeling Earth Systems and Environment 11, 312. https://doi.org/10.1007/s40808-025-02496-8 Dai, R., Hou, Y., Li, X., 2025. The optimization strategies for crop nutrient management in saline-alkali soil environments: From traditional farming techniques to the integrated application of smart agriculture. Advances in Resources Research 5, 1552–1570. Djibril, K.N.G., Yiika, L.P., Etutu, M.E.M.M., Eric, B.E., Mengu, E.E., Jean-Lavenir, N.M., Armstrong-Altrin, J.S., 2024. Geochemistry of sediments from the Mugheb River, Bamenda, Cameroon Volcanic Line: implications for provenance, paleoweathering and tectonic setting. Arabian Journal of Geosciences 17, 331. https://doi.org/10.1007/s12517-024-12148-3 Du, X., Du, Y., Feng, N., Zheng, D., Zhou, H., Huo, J., 2024. Exogenous Uniconazole promotes physiological metabolism and grain yield of rice under salt stress. Frontiers in Plant Science Volume 15-2024. https://doi.org/10.3389/fpls.2024.1459121 Dzulhijjah, D.A., Kusrini, ., Martinez-Bejar, R., 2025. Comparative Analysis of Machine Learning Models for Sentinel-2 based Classification of the Bornean Heath Forest. Eng. Technol. Appl. Sci. Res. 15, 21937–21943. https://doi.org/10.48084/etasr.10173 Edet, A., 2017. Hydrogeology and groundwater evaluation of a shallow coastal aquifer, southern Akwa Ibom State (Nigeria). Applied Water Science 7, 2397–2412. https://doi.org/10.1007/s13201-016-0432-1 El Hammioui, Y., Belghiti, M.L., Boutagayout, A., Hili, A., El Boute, S., Hilali, A., Azzirgue, E.M., Abba, E.H., 2025. Assessing the groundwater quality in the Khenifra Province, Morocco. Mediterranean Geoscience Reviews 7, 119–137. https://doi.org/10.1007/s42990-024-00141-7 El-Naggar, M.M., Fishar, M.R., Khalil, M.T., 2025. Climatic Variation and the Aquatic Biodiversity in Egypt, in: Climate Changes Impacts on Aquatic Environment: Assessment, Adaptation, Mitigation, and Road Map for Sustainable Development. Springer, pp. 65–84. El-Ramady, H., Prokisch, J., Mansour, H., Bayoumi, Y.A., Shalaby, T.A., Veres, S., Brevik, E.C., 2024. Review of Crop Response to Soil Salinity Stress: Possible Approaches from Leaching to Nano-Management. Soil Systems 8. https://doi.org/10.3390/soilsystems8010011 Fentahun, A., Mechal, A., Karuppannan, S., 2023. Hydrochemistry and quality appraisal of groundwater in Birr River Catchment, Central Blue Nile River Basin, using multivariate techniques and water quality indices. Environmental Monitoring and Assessment 195, 655. https://doi.org/10.1007/s10661-023-11198-6 Gad, M., Gaagai, A., Agrama, A.A., El-Fiqy, W.F., Eid, M.H., Szűcs, P., Elsayed, S., Elsherbiny, O., Khadr, M., Abukhadra, M.R., 2024. Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study. Heliyon 10. Goswami, S., Rai, A.K., 2024. Identifying intrusion of seawater in coastal aquifers by modified GALDIT (M-GALDIT) index. Groundwater for Sustainable Development 25, 101173. https://doi.org/10.1016/j.gsd.2024.101173 Grandika, D.S., Nuha, H.H., Putrada, A.G., 2024. Predicting Soil Moisture Levels with Long Short-Term Memory: A Deep Learning Approach Integrated with Internet of Things Data, in: 2024 International Conference on Decision Aid Sciences and Applications (DASA). pp. 1–6. https://doi.org/10.1109/DASA63652.2024.10836388 Guettaia, S., Boudjema, A., Derdour, A., Laoufi, A., Almohamad, H., Al-Mutiry, M., Abdo, H.G., 2025. Hydrochemical Characterization and Predictive Modeling of Groundwater Quality in Karst Aquifers Under Semi-Arid Climate: A Case Study of Ghar Boumaaza, Algeria. Sustainability 17. https://doi.org/10.3390/su17156883 He, Q., Xu, Y., Jin, Z., Hu, S., Pan, J., Zhao, P., Chi, R., 2025. Effect of sodium citrate on the expansion of clay minerals during leaching process of ionic rare-earth ores. Colloids and Surfaces A: Physicochemical and Engineering Aspects 137869. Huang, M., Qi, J., Zhang, C., Wang, Y., Chen, Y., Shao, J., Wu, S., 2025. Spatiotemporal Analysis of Sea-Surface pH in the Pacific Ocean Based on Interpretable Machine Learning. Journal of Marine Science and Engineering 13. https://doi.org/10.3390/jmse13071220 Igwebuike, N., Ajayi, M., Okolie, C., Kanyerere, T., Halihan, T., 2025. Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa. Earth Science Informatics 18, 6. Iqbal, J., Su, C., Ahmad, M., Baloch, M.Y.J., Rashid, A., Ullah, Z., Abbas, H., Nigar, A., Ali, A., Ullah, A., 2024. Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models. Environmental Geochemistry and Health 46, 14. Isık, H., Akkan, T., 2025. Water quality assessment with artificial neural network models: Performance comparison between SMN, MLP and PS-ANN methodologies. Arabian Journal for Science and Engineering 50, 369–387. Ismail, M., Pradhanang, S.M., Boving, T., Motta, S., McCarron, B., Volk, A., 2024. Review of modeling approaches at the freshwater and saltwater interface in coastal aquifers. Land 13, 1332. J, A., G, N., Sureshkumar, C., V, N., Kadry, S., 2024. Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions. Procedia Computer Science 235, 2–11. https://doi.org/10.1016/j.procs.2024.04.001 Jahan, S., Manzur, I., Shahria Alam, B.M., Rahman, T., Momo, I.J., Kibria, G., Yousuf Ali, Md.N., 2025. Optimizing Crop Selection Through Hyperparameter Tuning of Neural Networks: Development and Evaluation of Deep Learning Models for Enhancing Crop Recommendation Systems, in: 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). pp. 276–282. https://doi.org/10.1109/CSNT64827.2025.10968031 Jodhani, K.H., Gupta, N., Dadia, S., Patel, H., Patel, D., Jamjareegulgarn, P., Singh, S.K., Rathnayake, U., 2025. Sustainable groundwater management through water quality index and geochemical insights in Valsad India. Scientific Reports 15, 8769. https://doi.org/10.1038/s41598-025-92053-1 Kaiser, H.F., 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200. https://doi.org/10.1007/BF02289233 Kharwar, R.N., Prajapati, P., Sinha, S., 2025. Endophytic fungi mediated alleviation of salinity stress in crop plants: an insight and future prospect. Symbiosis 96, 1–28. https://doi.org/10.1007/s13199-025-01060-3 Kong, L., Chu, H., Hou, G., Zeng, J., Zhao, L., Han, X., Li, Y., Zhou, F., Yin, M., Zhang, X., 2025. Characteristics and mechanisms of sea (salt) water intrusion in Laizhou City, Shandong Province, China. Frontiers in Marine Science Volume 12-2025. https://doi.org/10.3389/fmars.2025.1578175 Kumar, S., Agarwal, M., Deshpande, V., Cooper, J.R., Khosravi, K., Rathnayake, N., Hoshino, Y., Kantamaneni, K., Rathnayake, U., 2024. AI-driven predictions of geophysical river flows with vegetation. Scientific reports 14, 16368. Leena Sri, R., Vetriveeran, D., Sambandam, R.K., Jenefa, J., Thangavel, K., 2025. Transformative Trends in AI for Environmental Monitoring: Challenges, Applications. Environmental Monitoring Using Artificial Intelligence 1–28. Liu, S.-S., Bai, Y.-N., Ni, J., He, Z., Yang, G.-P., 2025. Sources and Emissions of Marine-Derived Halocarbons Over the Coastal and Open Ocean of the Northwest Pacific. Journal of Geophysical Research: Atmospheres 130, e2024JD042706. https://doi.org/10.1029/2024JD042706 Lo Medico, F., Rizzo, P., Rotigliano, E., Celico, F., 2025. Groundwater Contamination: Study on the Distribution and Mobility of Metals and Metalloids in Soil and Rocks. International Journal of Environmental Research and Public Health 22. https://doi.org/10.3390/ijerph22020182 lotfinasabasl, S., Gohardoust, A., Dargahian, F., Zandifar, S., 2025. Comprehensive evaluation of groundwater quality and drought susceptibility in Jazmurian Basin Iran using integrated statistical GIS analysis. Scientific Reports 15, 11007. https://doi.org/10.1038/s41598-025-95839-5 Malal, H., Garcia, J.A., Marrs, A., Ait Hamza, M., Emerson, C., Nocco, M., Lakhtar, H., Lazcano, C., 2025. Organic and inorganic fertilizers modulate the response of the soil microbiome to salinity stress. Frontiers in Microbiology 16, 1551586. Malik, A., Yasar, A., Tabinda, A., Iftikhar, M., Mukhtar, S., 2024. Health risk assessment of nitrates and fluorides ingestion and geochemical evaluation of groundwater characteristics in semi-arid regions. International Journal of Environmental Science and Technology 21, 8459–8486. Manhou, K., Moussadek, R., Yachou, H., Zouahri, A., Douaik, A., Hilal, I., Ghanimi, A., Hmouni, D., Dakak, H., 2024. Assessing the Impact of Saline Irrigation Water on Durum Wheat (cv. Faraj) Grown on Sandy and Clay Soils. Agronomy 14. https://doi.org/10.3390/agronomy14122865 Manhou, K., Taghouti, M., Moussadek, R., Elyacoubi, H., Bennani, S., Zouahri, A., Ghanimi, A., Sanad, H., Oueld Lhaj, M., Hmouni, D., Dakak, H., 2025. Performance, Agro-Morphological, and Quality Traits of Durum Wheat (Triticum turgidum L. ssp. durum Desf.) Germplasm: A Case Study in Jemâa Shaïm, Morocco. Plants 14. https://doi.org/10.3390/plants14101508 Manimaran, D., Abishek, S.R., Pitchaimani, V.S., Ravindran, A.A., Soniyamary, M., Karuppannan, S., 2025. Spatial variation and hydrogeochemical characterization of groundwater in Vallanadu region: insights from water quality index and statistical analysis. Discover Sustainability 6, 524. https://doi.org/10.1007/s43621-025-01381-9 Miller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., Nowy, A., 2025. Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics 14. https://doi.org/10.3390/electronics14040696 Mondal, S., Saha, S., Das, S.R., Chatterjee, D., 2024. Impact of Conservation Agriculture on Soil Health and Environmental Sustainability, in: Pathak, H., Chatterjee, D., Saha, S., Das, B. (Eds.), Climate Change Impacts on Soil-Plant-Atmosphere Continuum. Springer Nature Singapore, Singapore, pp. 255–281. https://doi.org/10.1007/978-981-99-7935-6_10 Nthebere, K., Apoorva, M.R., Chandra, M.S., Yadav, M.B.N., Prakash, T.R., 2023. Farming Technologies and Carbon Sequestration Alternatives to Combat Climate Change Through Mitigation of Greenhouse Gas Emissions, in: Chatterjee, U., Shaw, R., Kumar, S., Raj, A.D., Das, S. (Eds.), Climate Crisis: Adaptive Approaches and Sustainability. Springer Nature Switzerland, Cham, pp. 253–275. https://doi.org/10.1007/978-3-031-44397-8_14 Ojo, O.T., Ike, C.J., Aladeboyeje, A.I., Olaseeni, O.G., Adewumi, O.F., 2025. Hydrochemical assessment of borehole water in Effurun, Delta state Niger Delta, Nigeria: implications for public health and safety. Discover Geoscience 3, 10. https://doi.org/10.1007/s44288-025-00117-6 Okebalama, C.B., Udeani, C.M., Awaogu, C.E., 2024. Aggregate Stability And The Influence Of Sodium Chloride On Dispersion Of Sandy Clay Loam Soils In Southeastern Nigeria. Global Journal of Agricultural Sciences 23, 49–60. Oueld Lhaj, M., Moussadek, R., Mouhir, L., Mdarhri Alaoui, M., Sanad, H., Iben Halima, O., Zouahri, A., 2024a. Assessing the Evolution of Stability and Maturity in Co-Composting Sheep Manure with Green Waste Using Physico-Chemical and Biological Properties and Statistical Analyses: A Case Study of Botanique Garden in Rabat, Morocco. Agronomy 14. https://doi.org/10.3390/agronomy14071573 Oueld Lhaj, M., Moussadek, R., Mouhir, L., Sanad, H., Manhou, K., Iben Halima, O., Yachou, H., Zouahri, A., Mdarhri Alaoui, M., 2025a. Application of Compost as an Organic Amendment for Enhancing Soil Quality and Sweet Basil (Ocimum basilicum L.) Growth: Agronomic and Ecotoxicological Evaluation. Agronomy 15. https://doi.org/10.3390/agronomy15051045 Oueld Lhaj, M., Moussadek, R., Zouahri, A., Sanad, H., Saafadi, L., Mdarhri Alaoui, M., Mouhir, L., 2024b. Sustainable Agriculture Through Agricultural Waste Management: A Comprehensive Review of Composting’s Impact on Soil Health in Moroccan Agricultural Ecosystems. Agriculture 14. https://doi.org/10.3390/agriculture14122356 Oueld Lhaj, Majda, Moussadek, R., Sanad, H., Manhou, K., Oueld Lhaj, M’hamed, Alaoui, M.M., Zouahri, A., Mouhir, L., 2025b. Ecological and Microbial Processes in Green Waste Co-composting for Pathogen Control and Evaluation of Compost Quality Index (CQI) Toward Agricultural Biosafety. https://doi.org/10.21203/rs.3.rs-7510199/v1 Panagos, P., Jones, A., Lugato, E., Ballabio, C., 2025. A Soil Monitoring Law for Europe. Global Challenges 9, 2400336. Perović, M., Obradović, V., Zuber-Radenković, V., Knoeller, K., Mitrinović, D., Čepić, Z., 2024. The comprehensive evaluation of nitrate origin and transformation pathways in the oxic alluvial aquifer in Serbia. Environmental Science and Pollution Research 31, 33030–33046. https://doi.org/10.1007/s11356-024-33403-w Perumal, M., Sekar, S., Carvalho, P.C., 2024. Global investigations of seawater intrusion (SWI) in coastal groundwaters in the last two decades (2000–2020): a bibliometric analysis. Sustainability 16, 1266. Philip, S., Nidhi, M., 2024. Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites. Materials Circular Economy 6, 34. https://doi.org/10.1007/s42824-024-00128-7 Poudel, B., Dahal, D., Banjara, M., Kalra, A., 2024. Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models. Forecasting 6, 1026–1044. https://doi.org/10.3390/forecast6040051 Rajeev, A., Shah, R., Shah, P., Shah, M., Nanavaty, R., 2025. The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction. Archives of Computational Methods in Engineering 32, 927–941. https://doi.org/10.1007/s11831-024-10156-w Regasa, A., Haile, W., Abera, G., 2025. Effects of lime and vermicompost application on soil physicochemical properties and phosphorus availability in acidic soils. Scientific Reports 15, 25544. https://doi.org/10.1038/s41598-025-02053-4 Rezaei, A., Hassani, H., 2018. Hydrogeochemistry study and groundwater quality assessment in the north of Isfahan, Iran. Environmental Geochemistry and Health 40, 583–608. https://doi.org/10.1007/s10653-017-0003-x Ristea, E., Bisinicu, E., Lavric, V., Parvulescu, O.C., Lazar, L., 2025. A Long-Term Perspective of Seasonal Shifts in Nutrient Dynamics and Eutrophication in the Romanian Black Sea Coast. Sustainability 17. https://doi.org/10.3390/su17031090 Rodier, J., 1985. L’analyse de l’eau, eaux naturelles, eaux résiduaires, eau de mer (7th ed.). Dunod, Paris. Saleh, M.A., Rasel, H., 2024. Machine learning for groundwater levels: Uncovering the best predictors. Sustainable Water Resources Management 10, 166. Sanad, H., Mouhir, L., Zouahri, A., Moussadek, R., El Azhari, H., Yachou, H., Ghanimi, A., Oueld Lhaj, M., Dakak, H., 2024a. Assessment of Groundwater Quality Using the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Multivariate Statistical Analysis (MSA), and GIS Approaches: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 16, 1263. https://doi.org/10.3390/w16091263 Sanad, H., Moussadek, R., Dakak, H., Zouahri, A., Oueld Lhaj, M., Mouhir, L., 2024b. Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 16, 2417. https://doi.org/10.3390/w16172417 Sanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Dakak, H., Manhou, K., Zouahri, A., 2025a. Monte carlo simulation for evaluating spatial dynamics of toxic metals and potential health hazards in sebou basin surface water. Scientific Reports 15, 29471. https://doi.org/10.1038/s41598-025-15006-8 Sanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Dakak, H., Zouahri, A., 2025b. Geospatial analysis of trace metal pollution and ecological risks in river sediments from agrochemical sources in Morocco’s Sebou basin. Scientific Reports 15, 16701. https://doi.org/10.1038/s41598-025-01199-5 Sanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Zahidi, K., Dakak, H., Manhou, K., Zouahri, A., 2025c. Ecological and Human Health Hazards Evaluation of Toxic Metal Contamination in Agricultural Lands Using Multi-Index and Geostatistical Techniques across the Mnasra Area of Morocco’s Gharb Plain Region. Journal of Hazardous Materials Advances 100724. https://doi.org/10.1016/j.hazadv.2025.100724 Sanad, H., Moussadek, R., Mouhir, L., Oueld Lhaj, M., Dakak, H., El Azhari, H., Yachou, H., Ghanimi, A., Zouahri, A., 2024c. Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Agronomy 14. https://doi.org/10.3390/agronomy14061112 Sanad, H., Oueld lhaj, M., Zouahri, A., Saafadi, L., Dakak, H., Mouhir, L., 2024d. Groundwater pollution by nitrate and salinization in Morocco: a comprehensive review. Journal of Water and Health 22, 1756–1773. https://doi.org/10.2166/wh.2024.024 Scanlon, B.R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R.Q., Jobbagy, E., Kebede, S., Kolusu, S.R., Konikow, L.F., Long, D., Mekonnen, M., Schmied, H.M., Mukherjee, A., MacDonald, A., Reedy, R.C., Shamsudduha, M., Simmons, C.T., Sun, A., Taylor, R.G., Villholth, K.G., Vörösmarty, C.J., Zheng, C., 2023. Global water resources and the role of groundwater in a resilient water future. Nature Reviews Earth & Environment 4, 87–101. https://doi.org/10.1038/s43017-022-00378-6 Serati, P., Sadatinejad, S.J., Yousefi, H., Mirzavand, M., 2025. Delineating the effect of water/rock–water/clay interactions and ion exchange in groundwater and soil salinization in an arid and semi-arid region of Iran. Environmental Earth Sciences 84, 405. https://doi.org/10.1007/s12665-025-12402-1 Seyar, M.H., Ahamed, T., 2024. Optimization of Soil-Based Irrigation Scheduling Through the Integration of Machine Learning, Remote Sensing, and Soil Moisture Sensor Technology, in: Ahamed, T. (Ed.), IoT and AI in Agriculture: Smart Automation Systems for Increasing Agricultural Productivity to Achieve SDGs and Society 5.0. Springer Nature Singapore, Singapore, pp. 275–299. https://doi.org/10.1007/978-981-97-1263-2_18 Shah, S.S., van Dam, J., Singh, A., Kumar, Suresh, Kumar, Satyendra, Bundela, D.S., Ritsema, C., 2025. Impact of irrigation, fertilizer, and pesticide management practices on groundwater and soil health in the rice–wheat cropping system—a comparison of conventional, resource conservation technologies and conservation agriculture. Environmental Science and Pollution Research 32, 533–558. Shqerat, N., Al-Tabbal, J., 2025. Potential reuse of greywater for irrigation of tomato (Solanum lycopersicum) plants and its effect on plants growth and soil. International Journal of Phytoremediation 27, 561–582. https://doi.org/10.1080/15226514.2024.2427926 Shu, P., Aslam, R.W., Naz, I., Ghaffar, B., Kucher, D.E., Quddoos, A., Raza, D., Abdullah-Al-Wadud, M., Zulqarnain, R.M., 2025. Deep learning-based super-resolution of remote sensing images for enhanced groundwater quality assessment and environmental monitoring in Urban Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Silwani, T., Themba, N., Chokwe, T.B., Semenya, K., 2025. Assessment of Groundwater Quality, Heavy Metal Contamination, and Human Health Risks in Roundhill Municipal Landfill, Eastern Cape, South Africa. Journal of Environmental Health and Sustainable Development 10, 2666–2693. Somay-Altas, M., Sanli, E., 2025. Urban Wetlands as EcoHaven Oasis: Hydrogeochemical Insights from the Inciralti-Cakalburnu Urban Wetland (ICUW) in Izmir, Turkiye. Water, Air, & Soil Pollution 236, 208. https://doi.org/10.1007/s11270-025-07840-3 Suh, G.C., Afahnwie, N.A., Tiabou, A.F., Djibril, K.N.G., Meniemoh, A.R., Yiika, L.P., 2025. Source apportionment, ecological and toxicological risk assessment of trace metals in agricultural soils of Wabane, South West Region, Cameroon. Journal of Trace Elements and Minerals 12, 100218. https://doi.org/10.1016/j.jtemin.2025.100218 Tackley, H.A., Kurylyk, B.L., Lake, C.B., 2025. Saltwater Intrusion Vulnerability of Soil and Groundwater Near Estuaries. Wiley Interdisciplinary Reviews: Water 12, e70031. Tefera, M.L., Carletti, A., Altea, L., Rizzu, M., Migheli, Q., Seddaiu, G., 2024. Land degradation and the upper hand of sustainable agricultural intensification in sub-Saharan Africa-A systematic review. Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS) 125, 63–83. Thakur, T., Rishi, M.S., Naik, P.K., Sharma, P., 2016. Elucidating hydrochemical properties of groundwater for drinking and agriculture in parts of Punjab, India. Environmental Earth Sciences 75, 467. https://doi.org/10.1007/s12665-016-5306-1 Tiabou, A.F., Atabe, G.A.A., Sigue, C., Yiika, L.P., Kachoueiyan, F., Forchenallah, N.E., 2024a. Appraisal of pollution, ecological and health risks assessment of trace metals in soils of Logbadjeck quarrying area, Nyong Series, Cameroon. Journal of Trace Elements and Minerals 10, 100204. https://doi.org/10.1016/j.jtemin.2024.100204 Tiabou, A.F., Esuka, C.N., Mbowou Ngantche, I.F., Yiika, L.P., Wadgua, B.P., Agyingi, C.M., 2024b. Spatial Distribution, Contamination Characteristics and Eco-Risk Assessment of Toxic Metals in Stream Sediments of Njombe-Penja Banana Plain, Cameroon Volcanic Line. Chemistry Africa 1–21. Tiabou, A.F., Manka’a, F.C., Yiika, L.P., Mengu Junior, E.E., 2025. Potentially toxic metal contamination, pollution, and ecological risk assessment in volcanic soils of Njombe-Penja Banana Plain, Cameroon Volcanic Line. Arabian Journal of Geosciences 18, 180. https://doi.org/10.1007/s12517-025-12317-y Wang, L., Ju, C., Han, C., Yu, Z., Bai, M., Wang, C., 2025. The interaction of nutrient uptake with biotic and abiotic stresses in plants. Journal of Integrative Plant Biology 67, 455–487. Wedding, L.M., Pittman, S., Lepczyk, C., Parrain, C., Puniwai, N., Boyle, J., Goldberg, E., Young, M., Marty, P., Wilhelm, K., 2024. Integrating the multiple perspectives of people and nature in place-based marine spatial planning. npj Ocean Sustainability 3, 43. Wen, S., Wen, M., Liang, S., Pang, G., Fan, J., Dong, M., Wang, Y., Zhang, J., Ye, Y., 2024. Spatial Distribution and Mechanisms of Groundwater Hardness in the Plain Area of Tangshan City, China. Water 16. https://doi.org/10.3390/w16243627 WHO, 2017. World Health Organization. Guidelines for Drinking-Water Quality: Fourth Edition Incorporating First Addendum. World Health Organization: Geneva, Switzerland. Wurl, J., Imaz-Lamadrid, M.A., Mendez-Rodriguez, L.C., Hernández-Morales, P., 2023. Hydrochemical Indicator Analysis of Seawater Intrusion into Coastal Aquifers of Semiarid Areas. Resources 12. https://doi.org/10.3390/resources12040047 Xu, Q., Yang, F., Hu, S., He, X., Hong, Y., 2024. Tree Height–Diameter Model of Natural Coniferous and Broad-Leaved Mixed Forests Based on Random Forest Method and Nonlinear Mixed-Effects Method in Jilin Province, China. Forests 15. https://doi.org/10.3390/f15111922 Yassin, M.A., Abba, S.I., Shah, S.M.H., Usman, A.G., Egbueri, J.C., Agbasi, J.C., Khogali, A., Baalousha, H.M., Aljundi, I.H., Sammen, S.Sha., Scholz, M., 2024. Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia. Water 16. https://doi.org/10.3390/w16101401 Zafar, S., Khan, M.K., Aslam, N., Hasnain, Z., 2024. Impact of Different Stresses on Morphology, Physiology, and Biochemistry of Plants, in: Shahid, M., Gaur, R. (Eds.), Molecular Dynamics of Plant Stress and Its Management. Springer Nature Singapore, Singapore, pp. 67–91. https://doi.org/10.1007/978-981-97-1699-9_4 Zahi, F., Drouiche, A., Medjani, F., Azzeddine, R., Djidel, M., 2024. Hydrogeochemical processes controlling surface water quality for irrigation in a Mediterranean wetland ecosystem, Northeast Algeria. Environmental Monitoring and Assessment 196, 881. https://doi.org/10.1007/s10661-024-13019-w Zeng, L., Yang, F., Chen, Y., Chen, S., Xu, M., Gu, C., 2024. Temperature and Dissolved Oxygen Drive Arsenic Mobility at the Sediment—Water Interface in the Lake Taihu. Toxics 12. https://doi.org/10.3390/toxics12070471 Zhang, Haitao, Jiang, B., Zhang, Haiqin, Li, P., Wu, M., Hao, J., Hu, Y., 2024. Water–Rock Interaction Mechanisms and Hydrochemical Evolution in the Underground Reservoirs of Coal Mines. ACS Omega 9, 43834–43849. https://doi.org/10.1021/acsomega.4c06809 Zhang, Q., You, X., 2024. Recent advances in surface water quality prediction using artificial intelligence models. Water Resources Management 38, 235–250. Zhang, Y., Li, H., Zhang, L., Xue, Y., 2025. Seawater intrusion impacts on groundwater and soil quality in the alluvial fan of the Xiaoling River, Liaoning, China. Human and Ecological Risk Assessment: An International Journal 1–23. Zouahri, A., Dakak, H., Douaik, A., El Khadir, M., Moussadek, R., 2015. Evaluation of groundwater suitability for irrigation in the Skhirat region, Northwest of Morocco. Environmental Monitoring and Assessment 4184. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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2","display":"","copyAsset":false,"role":"figure","size":379201,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial analysis of physico-chemical parameters.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/cf99b0a5c04173d4cfe46868.png"},{"id":92290438,"identity":"07df4006-19b0-4c6e-a32b-a8b1bfcefafe","added_by":"auto","created_at":"2025-09-26 20:45:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111780,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePearson correlation heatmap of groundwater physico-chemical parameters in the Skhirate coastal aquifer.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/bb8711838cbcc29bdf5b4daa.png"},{"id":92290436,"identity":"2d3e7848-e121-49dd-89ba-aa994bd706c7","added_by":"auto","created_at":"2025-09-26 20:45:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26868,"visible":true,"origin":"","legend":"\u003cp\u003ePCA biplot of standardized physico-chemical parameters in groundwater samples from the Skhirate coastal aquifer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/10b969b48818f548aa35ea36.png"},{"id":92289564,"identity":"d6a3d08f-19a3-4542-a88f-b0ccf7cdec19","added_by":"auto","created_at":"2025-09-26 20:36:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":57289,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of the \u003cem\u003eK-means clustering of GW samples (n = 30).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/35b20a7a7a5a6ac7ff57c38e.png"},{"id":92289517,"identity":"c9c6b000-a6f7-407e-91a3-3133160272c3","added_by":"auto","created_at":"2025-09-26 20:36:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":88116,"visible":true,"origin":"","legend":"\u003cp\u003ePiper trilinear diagram showing the hydrochemical facies of groundwater samples from the Skhirate coastal aquifer.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/18ddbc49f29ed34cb1d06255.png"},{"id":92290425,"identity":"26d1eed5-2d15-4715-a844-22cd0ad1cb75","added_by":"auto","created_at":"2025-09-26 20:45:19","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":85483,"visible":true,"origin":"","legend":"\u003cp\u003eGibbs diagrams illustrating the relationship between TDS and major ion ratios.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/c701be3eb6e860aae39a1c6f.png"},{"id":92289528,"identity":"32013cc2-fb68-4114-8626-2395c319acd1","added_by":"auto","created_at":"2025-09-26 20:36:49","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":49171,"visible":true,"origin":"","legend":"\u003cp\u003eChadha diagram representing the hydrogeochemical facies of groundwater samples in the Skhirate coastal area.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/237bf7fdda95b2dda48c0961.png"},{"id":92289506,"identity":"3bdb0c5e-97f0-4c21-a44c-c916d35f52de","added_by":"auto","created_at":"2025-09-26 20:36:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":167292,"visible":true,"origin":"","legend":"\u003cp\u003eGW samples quality classification (a) WQI, (b) IWQI and (c) SMI.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/c9f7428727aa6d5790a63e14.png"},{"id":92290424,"identity":"460a85c9-ec66-4361-8f72-c264a2959315","added_by":"auto","created_at":"2025-09-26 20:45:18","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":118747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDiagrams illustrating the irrigation suitability of groundwater samples (a) USSL diagram representing the irrigation suitability of groundwater samples based on SAR and EC\u003c/em\u003e and (b)\u003cem\u003e \u003c/em\u003eWilcox diagram showing the classification of groundwater samples based %Na versus EC.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/dac21f069b3380408552083e.png"},{"id":92289532,"identity":"8d6bfec0-a02d-472e-a981-f83d88c0d8a0","added_by":"auto","created_at":"2025-09-26 20:36:50","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":36315,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs illustrating (a) model performance comparison (RF vs ANN) and (b) actual vs predicted values of WQI, IWQI and SMI.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/3756b79f94c2afb7adbb21bf.png"},{"id":92289508,"identity":"d3324374-d16a-4876-9f62-bd6824ecaafa","added_by":"auto","created_at":"2025-09-26 20:36:47","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":198431,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution map of predicted values of (a) WQI, (b) IWQI and (c) SMI.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/dd319565c789edfde5d69bb2.png"},{"id":92290538,"identity":"5d0a9671-3be3-4698-a3e4-5fd80ffccc05","added_by":"auto","created_at":"2025-09-26 21:04:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3782479,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7705609/v1/bc161998-b208-41a7-b987-c62a1cd5373a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAI and Machine Learning-Based Spatial Modeling of Groundwater Quality Indices and Hydrogeochemistry for Accurate Prediction of Seawater Intrusion and Irrigation Sustainability in Coastal Agroecosystems\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGroundwater (GW) is the backbone of global water supply, providing nearly 60% of all drinking water and accounting for over 40% of irrigation water used in agriculture worldwide (Scanlon et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Its strategic importance is especially pronounced in coastal zones, where more than 40% of the global population resides within 100 kilometers of the ocean (Wedding et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These densely populated and economically productive areas are facing increasing stress on GW reserves due to the combined pressures of urbanization, agricultural intensification, and industrial activities. Coastal aquifers are particularly sensitive hydrogeological systems due to their proximity to the ocean and the natural hydraulic gradient that controls the freshwater-saltwater interface (Ismail et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Under normal conditions, freshwater in coastal aquifers forms a lens over the denser seawater. However, when the rate of GW extraction exceeds natural recharge, common in agricultural zones and urban centers, this delicate equilibrium is disrupted, allowing seawater to intrude inland (Perumal et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Climate change adds another dimension to this vulnerability, with rising sea levels, reduced precipitation, and increased evapotranspiration exacerbating the risks of aquifer salinization (Tackley et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Coastal GW quality thus becomes a critical concern, threatening not only human consumption but also food production and ecosystem services in these regions.\u003c/p\u003e\u003cp\u003eSeawater intrusion (SWI) is one of the most widespread forms of GW contamination in coastal areas. It occurs when saline water from the ocean encroaches into freshwater aquifers, driven by over-extraction, sea-level rise, and declining recharge rates (Zhang et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Globally, prominent SWI hotspots have been identified in the Nile Delta (Egypt) and much of the Mediterranean basin (El-Naggar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The consequences of SWI include deterioration of drinking water quality, soil salinization, crop failure, and loss of agricultural productivity (Panagos et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In Morocco, SWI represents a growing concern, particularly in the Gharb, Sa\u0026iuml;ss, and Souss-Massa basins regions where intensive agriculture and expanding urbanization place tremendous stress on GW resources (Sanad et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024d\u003c/span\u003e). Factors contributing to SWI in Morocco include poorly regulated GW abstraction, absence of recharge control infrastructure, and reliance on shallow aquifers for irrigation. The situation is aggravated by declining precipitation trends and increasing evapotranspiration due to higher temperatures, both linked to climate change (Chrif El Idrissi et al., 2025). Consequently, sustainable GW management and early warning systems become essential in mitigating the long-term impacts of SWI on water security and agricultural sustainability in Morocco's vulnerable coastal belts.\u003c/p\u003e\u003cp\u003eIntensive agriculture, while essential for food security, is a major driver of GW degradation in many coastal zones (Tefera et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). High dependency on GW for irrigation, coupled with unsustainable fertilization practices, contributes to the mobilization of salts and chemical residues into aquifers (Shah et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In semi-arid and arid regions, such as Morocco\u0026rsquo;s Atlantic coast, excessive abstraction of GW for year-round irrigation results in both water table decline and quality deterioration through seawater intrusion (Oueld Lhaj et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Moreover, the leaching of nitrates, phosphates, and agrochemicals into GW disrupts its potability and reduces its suitability for irrigation (Malik et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Salinization due to seawater intrusion has direct consequences for soil fertility. Elevated concentrations of sodium and chloride ions lead to ionic imbalances in the soil matrix, causing dispersion of soil particles, reduced permeability, and lower water infiltration (Okebalama et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; He et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These changes hinder plant root development and reduce nutrient uptake, significantly affecting crop yields and health (Dai et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The soil sodicity and salinity can impair microbial activity, degrade organic matter, and lead to irreversible declines in soil structure (Malal et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In our study region, where avocado, tomato, and other high-value crops dominate agricultural systems, such salinization processes could undermine long-term productivity and sustainability.\u003c/p\u003e\u003cp\u003eOver the last decade, artificial intelligence (AI) has emerged as a transformative tool in environmental monitoring, offering powerful techniques for data analysis, pattern recognition, and predictive modeling (Leena Sri et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In hydrogeology, machine learning algorithms such as Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) have been applied to simulate aquifer behavior, classify water quality, and detect pollution hotspots with high accuracy (Iqbal et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saleh and Rasel, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Igwebuike et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These approaches are particularly valuable when dealing with multivariate, nonlinear datasets common in groundwater systems. AI techniques are now being increasingly combined with traditional hydrogeochemical and geostatistical methods to enhance spatial predictions and decision-making (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, ANN models can capture complex nonlinear relationships between hydrochemical variables and water quality indices, while ensemble methods like RF can provide robust predictions even in small datasets with mixed variable importance (Isık and Akkan, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Integrating AI with GIS-based spatial interpolation tools such as Inverse Distance Weighting (IDW) and Kriging allows for the generation of high-resolution groundwater quality maps, supporting targeted management in sensitive coastal zones (Sanad et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite advances in GW monitoring and AI-based modeling, most studies in Morocco and similar semi-arid regions have focused on either hydrochemical assessment or basic geospatial mapping. Limited research has integrated multiple water quality indices such as the Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), and Saltwater Mixing Index (SMI) with advanced multivariate statistical methods like principal component analysis (PCA) and K-means clustering, as well as machine learning algorithms within a single framework to assess GW degradation due to seawater intrusion and agricultural stress (Gad et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Silwani et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This research fills an important gap by offering a comprehensive, data-driven framework for monitoring coastal water quality in the Skhirat region of Morocco. The methodology integrates hydrogeochemical assessment and statistical modeling with geospatial interpolation techniques (IDW), computes key water quality indices, and applies predictive modeling using RF and ANN. The outputs of these AI-based models are further spatially visualized to provide actionable insights into GW quality dynamics and the influence of seawater intrusion and agricultural pressures.\u003c/p\u003e\u003cp\u003eThe primary objectives of this study are: (1) to characterize the hydrogeochemical properties of GW and investigate their spatial patterns across the study area; (2) to perform advanced statistical analyses including correlation analysis, PCA, and K-means clustering to identify underlying geochemical processes and group similar water types; (3) to evaluate GW suitability for drinking and agricultural irrigation using established indices such as the WQI and IWQI; (4) to delineate and quantify the degree of seawater intrusion by applying both graphical methods (Piper, Gibbs, and Chadha diagrams) and the SMI; (5) to develop predictive models for WQI, IWQI, and SMI using ML techniques, specifically RF and ANN and (6) to generate high-resolution spatial distribution maps of predicted GW quality indices using GIS-based interpolation techniques to support targeted water resource management and mitigation strategies.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Sampling Location Description\u003c/h2\u003e\u003cp\u003eThe investigation was carried out in the Skhirat coastal zone, located between Rabat and Casablanca within the Rabat-Sal\u0026eacute;-K\u0026eacute;nitra region of Morocco \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This area is bounded by the Oued Ykem to the northeast, Oued Cherrat to the south, and the Atlantic Ocean to the west (Zouahri et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Geographically, it lies around 33\u0026deg;51\u0026prime;13\u0026Prime;N and 7\u0026deg;02\u0026prime;08\u0026Prime;W. The region is characterized by intensive horticultural activity, relying heavily on shallow groundwater for irrigation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt experiences a mild Mediterranean coastal climate with an average annual temperature of 17\u0026deg;C and annual rainfall varying from 250 to 800 mm. Geologically, the area comprises Paleozoic formations of shales, sandstones, and quartzites overlaid by Miocene and Plio-Quaternary calcareous sandstones and marls, forming permeable aquifers. Hydrogeologically, groundwater occurs within these Neogene deposits and shallow alluvial coastal aquifers, recharged mainly by runoff from the Ykem and Cherrat watersheds. The aquifer is phreatic, unconfined, and vulnerable to seawater intrusion due to its proximity to the Atlantic and overexploitation from agriculture.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. GW sampling protocol and analytical procedures\u003c/h2\u003e\u003cp\u003eGW quality monitoring was conducted in May 2025 at thirty selected locations to evaluate water suitability for both irrigation and domestic use. Sampling sites were systematically distributed based on topographical context, particularly targeting zones of intensive agriculture and areas in close proximity to the coastline \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The geographic position of each sampling point was precisely recorded using a Garmin Dakota 20 handheld GPS unit. On-site water quality measurements, including pH, electrical conductivity (EC), total dissolved solids (TDS), and dissolved oxygen (DO), were performed with a Bante 900P portable multiparameter probe to ensure real-time data collection (Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Water samples were passed through 0.45 \u0026micro;m membrane filters and collected into 500 mL polyethylene containers, which had been pre-rinsed with both distilled water and sample water to minimize any potential contamination. GW table depth at each site was measured using a piezometric probe with a 200-meter cable. The samples were maintained at approximately 4\u0026deg;C in insulated coolers and transported promptly to the laboratory. All procedures followed standard protocols for water quality sampling and analysis as outlined by (Kaiser, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). Laboratory analyses were performed in triplicate to guarantee data reliability, and the specific analytical techniques employed are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, following the guidelines of (Rodier, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLaboratory analysis instruments and methods for water quality parameters.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnalytical method\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEquipment model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium (K\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFlame photometry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eJenway PFP7 model apparatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003e(Rodier, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium (Na\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorides (Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMohr\u0026rsquo;s method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium (Ca\u003csup\u003e2+\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eComplexometric method (EDTA titration)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium (Mg\u003csup\u003e2+\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Hardness (TH)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbonate (CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTitrimetric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicarbonate (HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eDistillation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVELP SCIENTIFICA, Kjeldahl distillation unit, UDK 129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmmonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUV-visible spectrophotometry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJENWAY 6405 model (880 nm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSulphate (SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNephelometric method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJENWAY6405 Model (650 nm)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Statistical Analyses, Multivariate Techniques and Geospatial Mapping\u003c/h2\u003e\u003cp\u003eTo explore the spatial and hydrochemical behavior of GW in the study area, a combination of statistical and geospatial methods was employed. First, descriptive statistics were computed for all physico-chemical parameters using IBM SPSS v25. This provided a general overview of groundwater quality variability and allowed for initial assessment of potential anomalies. Pearson\u0026rsquo;s correlation matrix was then constructed to identify the strength and direction of linear associations between key variables, offering insights into common sources or linked geochemical processes. Multivariate statistical analysis included Principal Component Analysis (PCA) and K-means clustering. PCA reduced data dimensionality, highlighting principal factors controlling groundwater chemistry (e.g., seawater intrusion, agricultural leaching), while K-means clustering grouped groundwater samples into hydrochemical classes based on similarity in composition. These machine learning methods were implemented in Python using Scikit-learn and Seaborn libraries for advanced visualization (Bala Dhandayuthapani, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For geospatial interpretation IDW interpolation using ArcGIS 10.8 was applied to map the spatial distribution of GW parameters and indices across the region (Sanad et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025c\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Computation of Water Quality Indices\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Evaluation of the appropriateness of GW for domestic use and irrigation practices\u003c/h2\u003e\u003cp\u003eThe WQI was calculated using the weighted arithmetic method, where each parameter was assigned a weight based on its relative importance to human health, following WHO (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) standards (WHO, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; El Hammioui et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The index was computed using the weighted arithmetic method. The index consolidates multiple physicochemical parameters into a single score. Each parameter was assigned a weight (wi) based on its relative importance, and a quality rating scale (qi) was calculated using following Equations (1) to (4) as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Shu et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGW quality indicators and calculation formula.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuality rating scale (Qi)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeight (Wi)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWQI parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEquations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEq.No.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026ldquo;Wi\u0026rdquo; represents the relative weight of each parameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{W}\\mathbf{i}=\\frac{\\mathbf{w}\\mathbf{i}}{{\\sum\\:}_{\\mathbf{i}=\\mathbf{n}}^{\\mathbf{n}}\\mathbf{w}\\mathbf{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e(1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026ldquo;Qi\u0026rdquo; is the quality rating scale\u003c/p\u003e\u003cp\u003e\u0026ldquo;Ci\u0026rdquo; is the measured concentration of the parameter\u003c/p\u003e\u003cp\u003e\u0026ldquo;Si\u0026rdquo; standard permissible value (WHO, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{Q}\\mathbf{i}=\\frac{\\mathbf{C}\\mathbf{i}}{\\mathbf{S}\\mathbf{i}}\\:\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e(2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026ldquo;SI\u0026rdquo; sub-index for each parameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{S}\\mathbf{I}=\\mathbf{Q}\\mathbf{i}\\:\\times\\:\\mathbf{W}\\mathbf{i}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003e(3)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u0026ldquo;WQI\u0026rdquo; scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mathbf{W}\\mathbf{Q}\\mathbf{I}=\\:{\\sum\\:}_{\\mathbf{i}=\\mathbf{n}}^{\\mathbf{n}}\\mathbf{S}\\mathbf{I}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003e(4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWQI values were classified as Excellent (0\u0026ndash;50), Good (50\u0026ndash;100), Poor (100\u0026ndash;200), Very Poor (200\u0026ndash;300) and Unsuitable (\u0026gt;\u0026thinsp;300) (Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe IWQI was computed to determine the appropriateness of groundwater for irrigation by integrating a suite of agro-chemical indicators including EC, SAR, RSC, %Na, MAR, PI, RSBC, KI and PS (Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). All calculations followed standard equations commonly adopted in irrigation water quality studies \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIWQ indices and mathematical formula.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrrigation indices equations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEq. No.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{S}\\varvec{A}\\varvec{R}=\\frac{{\\varvec{N}\\varvec{a}}^{+}}{\\sqrt{\\frac{{(\\varvec{C}\\varvec{a}}^{2+}+{\\varvec{M}\\varvec{g}}^{2+})}{2}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{R}\\varvec{S}\\varvec{C}=\\:\\left[{\\varvec{H}\\varvec{C}\\varvec{O}}_{3}^{-}+\\:{\\varvec{C}\\varvec{O}}_{3}^{2-}\\right]-{[\\varvec{C}\\varvec{a}}^{2+}+{\\varvec{M}\\varvec{g}}^{2+}]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\%}\\varvec{N}\\varvec{a}=\\:\\frac{\\left({\\varvec{N}\\varvec{a}}^{+}+{\\varvec{K}}^{+}\\right)\\times\\:100}{{\\varvec{C}\\varvec{a}}^{2+}+{\\varvec{M}\\varvec{g}}^{2+}+\\:{\\varvec{N}\\varvec{a}}^{+}+{\\varvec{K}}^{+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{M}\\varvec{A}\\varvec{R}=\\frac{{\\varvec{M}\\varvec{g}}^{2+}\\:\\times\\:100}{\\left({\\varvec{C}\\varvec{a}}^{2+}+\\:{\\varvec{M}\\varvec{g}}^{2+}\\right)}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{P}\\varvec{I}=\\frac{\\left(\\sqrt{{\\varvec{H}\\varvec{C}\\varvec{O}}_{3}^{-}}+\\:{\\varvec{N}\\varvec{a}}^{+}\\right)\\times\\:100}{{({\\varvec{N}\\varvec{a}}^{+}+\\:\\varvec{C}\\varvec{a}}^{2+}+{\\varvec{M}\\varvec{g}}^{2+})}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{R}\\varvec{S}\\varvec{B}\\varvec{C}=\\:{\\varvec{H}\\varvec{C}\\varvec{O}}_{3}^{-}-\\:{\\varvec{C}\\varvec{a}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{K}\\varvec{R}=\\frac{{\\varvec{N}\\varvec{a}}^{+}}{{\\:\\varvec{C}\\varvec{a}}^{2+}+{\\varvec{M}\\varvec{g}}^{2+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{P}\\varvec{S}={\\varvec{C}\\varvec{l}}^{-}+\\:\\frac{1}{2}\\:\\times\\:{\\varvec{S}\\varvec{O}}_{4}^{2-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe IWQI classification system grouped samples into excellent (\u0026lt;\u0026thinsp;50), good (50\u0026ndash;100), poor (100\u0026ndash;200), very poor (200\u0026ndash;300) and unsuitable (\u0026gt;\u0026thinsp;300) categories (Sanad et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Seawater intrusion assessment using Saltwater Mixing Index (SMI)\u003c/h2\u003e\u003cp\u003eThis index uses a cumulative probability approach to establish regional thresholds for conservative ions (Na⁺, Mg\u0026sup2;⁺, Cl⁻, SO₄\u0026sup2;⁻) (Chandrajith et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thresholds were derived from a reference freshwater group, and the SMI equation was used to calculate mixing levels across all samples. It is calculated as follows:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:SMI=a\\:\\times\\:\\:\\frac{{C}_{{Na}^{+}}}{{T}_{{Na}^{+}}}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\:\\times\\:\\:\\frac{{C}_{{Mg}^{2+}}}{{T}_{{Mg}^{2+}}}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\:\\times\\:\\:\\frac{{C}_{{Cl}^{-}}}{{T}_{{Cl}^{-}}}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\:\\times\\:\\:\\frac{{C}_{{SO}_{4}^{2-}}}{{T}_{{SO}_{4}^{2-}}}\\)\u003c/span\u003e\u003c/span\u003e (13)\u003c/p\u003e\u003cp\u003ewhere (a\u0026thinsp;=\u0026thinsp;0.31, b\u0026thinsp;=\u0026thinsp;0.04, c\u0026thinsp;=\u0026thinsp;0.57, d\u0026thinsp;=\u0026thinsp;0.08) represent the relative concentration proportion of ions Na\u003csup\u003e+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e, Cl\u003csup\u003e\u0026minus;\u003c/sup\u003e, and SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e in seawater respectively (Wurl et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). C represents the concentration (in mg/L) of the ions in the sampled GW. \u0026ldquo;T\u0026rdquo; represent the regional freshwater thresholds and were estimated using cumulative probability analysis from non-saline samples in the study area.\u003c/p\u003e\u003cp\u003eSamples were categorized as \u0026ldquo;freshwater dominant\u0026rdquo; (SMI\u0026thinsp;\u0026lt;\u0026thinsp;1) or \u0026ldquo;saltwater dominant\u0026rdquo; (SMI\u0026thinsp;\u0026gt;\u0026thinsp;1) (Edet, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Application of Artificial Intelligence (AI) and Machine Learning (ML) Models for Predicting GW Quality Indices\u003c/h2\u003e\u003cp\u003eIn this study, supervised ML models were implemented to predict three GW quality indices (WQI, IWQI, and SMI) using key physicochemical parameters as input features. Two algorithms were employed and compared including Random Forest (RF) and Artificial Neural Network (ANN), both widely used in water quality modeling due to their flexibility and ability to capture nonlinear relationships (Kumar et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang and You, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Acharki et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1. Input Dataset and Preprocessing\u003c/h2\u003e\u003cp\u003eThe input dataset included measured all GW parameters. These were selected as predictors for WQI, IWQI, and SMI. Data were normalized using min\u0026ndash;max scaling to ensure consistent learning by the ANN and to prevent bias toward variables with larger scales.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2. Random Forest and Artificial Neural Network (ANN) Model\u003c/h2\u003e\u003cp\u003eThe RF model, a robust ensemble of decision trees, was applied with optimized parameters including number of trees (n_estimators) and maximum tree depth (Xu et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The model was trained using an 80:20 train-test split (Dzulhijjah et al., 2025). Model performance was evaluated using standard metrics (Coefficient of determination (R\u0026sup2;), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE)) (Grandika et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). ANN modeling was conducted using a feedforward backpropagation network with three hidden layers. The number of neurons was optimized through cross-validation. Activation functions used were ReLU (for hidden layers) and linear (for output layer) (Jahan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The model was trained using the Adam optimizer and mean squared error (MSE) loss function (J et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Early stopping was applied to prevent overfitting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.5.3. Model Comparison, Validation, and Spatial Mapping\u003c/h2\u003e\u003cp\u003eTo assess and compare the predictive capabilities of the machine learning models, both the RF and ANN algorithms were evaluated using standard performance metrics, including the R\u0026sup2;, RMSE, and MAE (Boutahri and Tilioua, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Poudel et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Predicted values of the GW quality indices (WQI, IWQI, and SMI) were compared against observed values using scatter plots of predicted vs. actual data to visualize model performance and assess fit quality across different models. Subsequently, the predicted outputs of the indices were georeferenced and spatially interpolated using the IDW method in ArcGIS 10.8.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Spatio-Statistical Evaluation of GW Quality Indicators in the Context of Seawater Intrusion and Agricultural Inputs\u003c/h2\u003e\n \u003cp\u003eGroundwater pH reflects the acidic or alkaline nature of the water and influences solubility and mobility of ions. In this study, pH values ranged from 6.91 (sample P3) to 7.73 (sample P8), with a mean of 7.33, remaining within WHO\u0026apos;s acceptable range \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistical analysis results of physico-chemical parameters.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWHO \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e Values\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStd Dev\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC (mS/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTDS (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e850.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1183.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0,8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDO (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e284.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e643.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e430.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e165.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTH (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e609.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e453.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaCO\u003csub\u003e3\u003c/sub\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e587.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e367.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe spatial map indicates relatively stable pH values across the coastal strip with minor acidification observed in the southeastern zone, possibly linked to localized agricultural acidification or soil-water interactions \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. EC, a proxy for total ion content and salinity, varied significantly from 1.36 to 6.30 mS/cm, with a high CV% of 35.45%, reflecting substantial heterogeneity in salinity levels. High EC zones, as illustrated in the map, are concentrated near the coastal fringe (samples P13, P14, and P21), suggesting direct seawater intrusion. The mean EC (2.62 mS/cm) exceeds the WHO threshold (1 mS/cm) in over 90% of samples, confirming widespread salinization.\u003c/p\u003e\n \u003cp\u003eTDS followed a similar pattern (850.90\u0026ndash;1521.00 mg/L), with 100% of samples exceeding the WHO standard of 500 mg/L, again reinforcing the intrusion of saline waters. TDS peaks in the western coastal zone align spatially with EC hot spots and proximity to the Atlantic Ocean. DO, vital for redox processes, exhibited low values (0.50\u0026ndash;3.36 mg/L), with a mean of 1.42 mg/L and high variability (CV% = 62.01%). These values reflect oxygen depletion likely due to the biological activity from fertilizers and organic inputs, especially in inland agricultural areas (Ristea et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The depressed DO levels are consistent with groundwater under reducing conditions, potentially enhancing mobilization of elements like Fe and Mn (Lo Medico et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegarding K⁺, which originates from fertilizers and weathering, values varied from 0.6 to 17.3 mg/L, exceeding the WHO limit of 10 mg/L in 17% of samples. The high CV% (91.27%) and spatial concentration of elevated K⁺ near sample P13 and P23 highlight zones impacted by intensive agriculture. Na⁺ ranged widely between 108.12 mg/L and 749.81 mg/L (mean\u0026thinsp;=\u0026thinsp;278.22 mg/L), with 100% of samples above the WHO limit (200 mg/L). Its strong spatial gradient toward the northern coast (notably P5 and P13) supports seawater intrusion as the primary source. Cl⁻, a conservative tracer of marine origin, revealed alarming levels between 224.11 and 1773.26 mg/L. Over 90% of samples exceed the WHO threshold (250 mg/L). The spatial map indicates maximum concentrations near samples P13, P16, and P24, strongly coinciding with the Atlantic margin. The sharp salinity gradients and elevated Na⁺/Cl⁻ reinforce that seawater intrusion is a major driver of groundwater degradation in the study area (Perumal et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCa\u0026sup2;⁺ and Mg\u0026sup2;⁺ contribute to water hardness and originate from both seawater mixing and carbonate weathering (Rezaei and Hassani, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Ca\u0026sup2;⁺ ranged from 64 to 340 mg/L, and Mg\u0026sup2;⁺ from 48 to 129.6 mg/L, both exceeding WHO limits (75 mg/L for Mg\u0026sup2;⁺ and 100 mg/L for Ca\u0026sup2;⁺) in 80% of samples. Spatially, elevated Ca\u0026sup2;⁺ concentrations appear inland (P16, P24), whereas Mg\u0026sup2;⁺ concentrations cluster along the coast (P13, P14), suggesting a mixed influence from marine intrusion and lithological inputs. Total hardness (TH), a cumulative index of Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺, ranged from 165.6 to 477.6 mg/L with a mean of 288.1 mg/L, placing the water in the \u0026ldquo;very hard\u0026rdquo; category in more than 75% of samples. These high values compromise water suitability for irrigation and domestic use, consistent with strong salinization and geological contributions (lotfinasabasl et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eCarbonates and bicarbonates regulate buffering capacity and reflect equilibrium with geological formations. CO₃\u0026sup2;⁻ ranged from 123 to 283.5 mg/L and HCO₃⁻ from 260.45 to 630.2 mg/L. CaCO₃ equivalent ranged from 232.5 to 507.5 mg/L. These elevated values indicate substantial carbonate buffering, possibly from weathering of limestone or dolomite units underlying the aquifer. The highest HCO₃⁻ concentrations are observed inland (P2, P29), while lower levels occur near the coast, where seawater dilution overrides carbonate buffering. These patterns suggest that carbonate levels are more influenced by rock\u0026ndash;water interactions in the inland zones than by saline sources (Serati et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eNitrate (NO₃⁻), a mobile indicator of fertilizer use and organic pollution, ranged from 6.23 to 111.56 mg/L, with 33% of samples surpassing the WHO guideline of 50 mg/L. High NO₃⁻ zones (P1, P26, P27) are inland and spatially align with agricultural zones, confirming fertilizer leaching. The average NO₃⁻ value (39.1 mg/L) and a moderate CV% suggest widespread anthropogenic inputs. Ammonium (NH₄⁺), often found under reducing conditions or from manure infiltration, ranged from 0.5 to 5.4 mg/L. Over 60% of samples exceed the typical natural background level of 0.5 mg/L. Peaks are observed in samples P1, P4, and P20, where both agricultural runoff and organic matter degradation are likely contributing factors (Oueld Lhaj et al., \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Phosphorus (P), usually present at trace levels in natural groundwater, showed concentrations ranging from 0.12 to 2.35 mg/L (mean\u0026thinsp;=\u0026thinsp;0.87 mg/L). Over 50% of samples exceed the typical limit of 0.1\u0026ndash;0.3 mg/L. The highest concentrations (P26, P27) are located in intensive cultivation areas and reflect phosphate fertilizer use. Sulfate (SO₄\u0026sup2;⁻) ranged from 202.94 to 465.98 mg/L with a mean of 308.6 mg/L, exceeding the WHO limit (250 mg/L) in 60% of samples. The spatial map highlights SO₄\u0026sup2;⁻ enrichment in coastal samples (P13, P14) and central zones (P20), indicating a combined effect of seawater mixing and agrochemical application (Sanad et al., \u003cspan class=\"CitationRef\"\u003e2024d\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Multivariate Correlation Analysis of GW Physico-Chemical Parameters to Elucidate Salinization and Agricultural Impacts\u003c/h2\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1. Pearson correlation\u003c/h2\u003e\n \u003cp\u003eThe correlation analysis provides critical insights into the relationships among the measured groundwater parameters, helping to elucidate shared origins, geochemical interactions, and potential contamination sources \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eA strong and significant correlation is observed between electrical conductivity (EC) and major cations/anions such as Na⁺ (r\u0026thinsp;=\u0026thinsp;0.85), Cl⁻ (r\u0026thinsp;=\u0026thinsp;0.84), K⁺ (r\u0026thinsp;=\u0026thinsp;0.77), and SO₄\u0026sup2;⁻ (r\u0026thinsp;=\u0026thinsp;0.73). These high correlations confirm that EC is primarily controlled by the concentration of dissolved salts, which are typical indicators of saline intrusion in coastal aquifers (Manhou et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). EC also correlates strongly with TDS (r\u0026thinsp;=\u0026thinsp;0.70), supporting its use as a proxy for salinity. The Na⁺\u0026ndash;Cl⁻ correlation (r\u0026thinsp;=\u0026thinsp;0.61) is also strong and supports the marine origin of salinization, as these ions dominate seawater composition. Additionally, the correlation between Cl⁻ and SO₄\u0026sup2;⁻ (r\u0026thinsp;=\u0026thinsp;0.64) suggests co-mobilization under saline water mixing and potential agricultural runoff containing sulfate-based fertilizers. Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺, the primary contributors to water hardness, show a very strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.98), and both are highly correlated with total hardness (TH), with r\u0026thinsp;=\u0026thinsp;0.98 for Ca\u0026sup2;⁺ and r\u0026thinsp;=\u0026thinsp;0.75 for Mg\u0026sup2;⁺. These results indicate a shared geochemical source, likely carbonate and dolomitic rock dissolution, enhanced by cation exchange reactions intensified by saline water encroachment (Brhane and Mekonen, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The moderate correlations of TH with EC (r\u0026thinsp;=\u0026thinsp;0.74) and Cl⁻ (r\u0026thinsp;=\u0026thinsp;0.69) further support the contribution of ion exchange processes induced by seawater intrusion, which tend to increase Ca\u0026sup2;⁺ in groundwater due to exchange with Na⁺. HCO₃⁻ and CO₃\u0026sup2;⁻, despite their geochemical interdependence, show only weak correlations with other ions. Their correlation with EC is weak (r = \u0026minus;\u0026thinsp;0.28 and r = \u0026minus;\u0026thinsp;0.14, respectively), suggesting that carbonate equilibrium processes are not directly linked to salinity gradients. This may be due to spatial variability in buffering capacity or anthropogenic inputs disrupting the natural carbonate balance (Huang et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the inverse relationships between HCO₃⁻ and Na⁺, Cl⁻, and SO₄\u0026sup2;⁻ indicate that carbonate-buffered inland zones are less impacted by seawater mixing, which typically dilutes bicarbonate concentrations. The strong positive correlation between NO₃⁻ and NH₄⁺ (r\u0026thinsp;=\u0026thinsp;0.71) and their even stronger association with P (r\u0026thinsp;=\u0026thinsp;0.92 for NH₄⁺ and r\u0026thinsp;=\u0026thinsp;0.71 for NO₃⁻) clearly reflect a common source of contamination: agricultural fertilizers and possibly manure application. These three parameters are weakly correlated with EC and Cl⁻, indicating that agricultural impact is spatially and chemically distinct from seawater intrusion. This pattern suggests localized contamination by nitrogenous and phosphate fertilizers in agricultural zones (notably sites P26, P27), consistent with earlier spatial analyses of nutrient distribution. DO is negatively correlated with most other parameters, particularly NH₄⁺ (r = \u0026minus;\u0026thinsp;0.72), P (r = \u0026minus;\u0026thinsp;0.67), and TDS (r = \u0026minus;\u0026thinsp;0.63). This inverse pattern reflects redox-sensitive conditions, where oxygen is consumed during microbial degradation of organic material and ammonification (Perović et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). It indicates zones with low oxygen and high nutrient loading, typical of areas affected by intensive agriculture and organic waste infiltration. Low DO also suggests reduced conditions, which can enhance the mobility of metals and nutrients, degrading groundwater quality (Zeng et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pH exhibits weak to moderate negative correlations with most major ions (e.g., Cl⁻ r = \u0026minus;\u0026thinsp;0.26, Na⁺ r = \u0026minus;\u0026thinsp;0.30, K⁺ r = \u0026minus;\u0026thinsp;0.25), indicating mild acidification in zones impacted by seawater intrusion or fertilizer reactions. The weak correlation with EC (r = \u0026minus;\u0026thinsp;0.49) suggests that pH variations are likely influenced by localized geochemical buffering, organic acids, and not strongly driven by overall salinity (Manhou et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, pH correlates positively with DO (r\u0026thinsp;=\u0026thinsp;0.36), suggesting that aerobic environments maintain higher alkalinity, while reducing conditions (often nutrient-loaded) tend to lower pH.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2. Principal Component Analysis (PCA)\u003c/h2\u003e\n \u003cp\u003eThe PCA was conducted to reduce the dimensionality of the physico-chemical groundwater dataset and to identify the dominant factors influencing groundwater quality in the Skhirate coastal aquifer \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e (Bhushan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe first two principal components (PC1 and PC2) accounted for 65.8% of the total variance, with PC1 explaining 44.4% and PC2 21.4%. PC1 was strongly and positively correlated with EC, TDS, Na⁺, Cl⁻, SO₄\u0026sup2;⁻, K⁺, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, and TH, indicating a clear salinization gradient likely driven by seawater intrusion and geogenic mineralization processes (Yassin et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). This component reflects the contribution of dissolved ionic species that dominate the hydrochemical signature of marine-influenced groundwater. Conversely, PC2 revealed a distinct nutrient pollution and redox-sensitive axis, positively associated with dissolved oxygen (DO) and negatively correlated with NH₄⁺, NO₃⁻, and PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u003c/sup\u003e. This trend highlights oxygen-depleted zones enriched with reactive nitrogen and phosphorus, typically linked to agricultural runoff, organic matter degradation, and fertilizer leaching (Chamoli et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The spatial separation of salinity and nutrient variables across the two components suggests the presence of dual contamination sources including geogenic (marine intrusion) and anthropogenic (agricultural inputs). Moreover, the relatively neutral loading of pH and HCO₃⁻ implies their buffering role in transitional geochemical zones. Overall, PCA confirms that both natural hydrogeochemical processes and intensive agricultural practices play pivotal roles in shaping groundwater quality in this vulnerable coastal region.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.3. K-means clustring\u003c/h2\u003e\n \u003cp\u003eThe application of K-means clustering (k\u0026thinsp;=\u0026thinsp;3) to the standardized dataset of 17 physico-chemical groundwater parameters successfully classified the 30 samples into three statistically distinct clusters. The clustering was visualized in a two-dimensional principal component space, where the first two principal components (PC1 and PC2) accounted for 65.8% of the total variance in water quality. Each cluster represents a group of groundwater samples with similar hydrochemical profiles, providing insight into the spatial and environmental processes affecting groundwater quality in the study area \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eCluster 1, predominantly located in the upper left quadrant of the PCA biplot, includes samples characterized by moderate concentrations of salinity-related parameters (Na⁺, Cl⁻, EC, TDS) and relatively balanced nutrient levels. These samples likely represent zones where groundwater is influenced primarily by natural geochemical interactions, such as water-rock interaction and carbonate dissolution, without significant anthropogenic stress (Jodhani et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The moderate placement along PC1 and minimal loading on PC2 indicate stable oxic conditions and limited agricultural input. Cluster 2 appears clustered in the lower left quadrant of the PCA plane and is clearly separated from the other clusters along PC2. It groups samples that are enriched in NH₄⁺, NO₃⁻, and P, and have relatively low DO concentrations, pointing to intensive agricultural influence. This group reflects groundwater affected by nitrate leaching from fertilizers, infiltration of organic waste, and ammonification under reducing conditions (Oueld Lhaj et al., \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e). The low positioning along the PC2 axis confirms suboxic to anoxic conditions within these samples, which are often encountered in intensively cultivated regions with poor drainage and high irrigation inputs. Cluster 3 is mainly located in the right half of the PCA biplot and corresponds to samples with elevated levels of EC, TDS, Na⁺, Cl⁻, Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, SO₄\u0026sup2;⁻, and TH. This cluster represents saline or highly mineralized groundwater, and its alignment along PC1 indicates strong geogenic or marine influence. The location and water quality characteristics suggest seawater intrusion or saline upwelling from deeper aquifers, particularly in wells closer to the Atlantic coast. The chemical signature of this group reflects ion exchange processes and mixing between fresh and saline waters, common in over-exploited coastal aquifers (Kong et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Hydrogeochemical Diagrams for Seawater Intrusion and Salinity Assessment\u003c/h2\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1. Hydrochemical Facies Characterization Using Piper Diagram\u003c/h2\u003e\n \u003cp\u003eThe Piper trilinear diagram provides valuable insights into the hydrochemical facies and dominant geochemical processes affecting the groundwater in the Skhirate coastal aquifer (Somay-Altas and Sanli, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). As depicted in the diagram, the analysis of the 30 groundwater samples reveals a clear and consistent dominance of the Ca\u0026sup2;⁺\u0026ndash;Cl⁻ water type, classifying the entire dataset within a single hydrochemical facies \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe anion triangle shows that all groundwater samples cluster strongly towards the chloride apex, indicating a pronounced enrichment in chloride ions across the aquifer. This distinctive distribution is a well-established geochemical signature of saline water intrusion, particularly in coastal settings where the hydraulic gradient is disturbed due to excessive groundwater pumping for irrigation practices. The displacement of fresh water by denser saline water results in chloride enrichment, making Cl⁻ a key tracer in detecting the advancement of seawater inland. On the cation triangle, the majority of the samples do not exhibit a single dominant cation, instead plotting within the \u0026ldquo;no dominant cation\u0026rdquo; field, which reflects a mixed Ca\u0026sup2;⁺\u0026ndash;Mg\u0026sup2;⁺\u0026ndash;Na⁺ composition. This pattern suggests ongoing cation exchange reactions, likely involving the replacement of Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ with Na⁺ in the aquifer matrix. These processes are typically associated with salinity encroachment and long-term geochemical evolution of groundwater. A notable exception is sample P3, which plots distinctly in the magnesium (Mg\u0026sup2;⁺) dominant field, indicating localized variations in lithology or water\u0026ndash;rock interaction. This sample may reflect areas where dolomitic dissolution or magnesium-rich fertilizers influence groundwater chemistry (Ojo et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe prevalence of Ca\u0026sup2;⁺\u0026ndash;Cl⁻ facies across the study area points toward regional-scale saline water mixing, likely linked to the proximity to the Atlantic Ocean and overexploitation of aquifers for irrigation. The uniform chloride dominance coupled with non-specific cation control (except for P3) suggests that diffuse seawater intrusion is a key process, rather than a sharp saline front. The groundwater chemistry is further influenced by ion exchange, rock\u0026ndash;water interaction, and possibly return flows from agricultural fields that reintroduce salts and alter ionic ratios (Sanad et al., \u003cspan class=\"CitationRef\"\u003e2024c\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2. Hydrogeochemical Processes Controlling Groundwater Chemistry using Gibbs Diagrams\u003c/h2\u003e\n \u003cp\u003eThe Gibbs diagrams serve as diagnostic tools to infer the dominant processes that govern groundwater chemistry, precipitation dominance, rock-water interaction, or evaporation\u0026ndash;crystallization dominance \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e (Zahi et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the left diagram, nearly all the groundwater samples fall within the rock dominance zone and cluster between 0.4 and 0.1 on the x-axis, with TDS values ranging from 800 to 1500 mg/L. This positioning suggests that water\u0026ndash;rock interaction is the principal mechanism influencing ion concentrations (Zhang et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The relatively high Cl⁻ proportions may further indicate the possible contribution of marine-derived salts, consistent with coastal seawater intrusion processes. The elevated Cl⁻/HCO₃⁻ ratios support the hypothesis of salinization due to saline water mixing, most likely originating from seawater encroachment. Similarly, the right diagram shows that all samples are also concentrated within the rock dominance field. This indicates cation exchange reactions and dissolution of silicate and carbonate minerals as major controlling factors. The moderate to high relative enrichment of Na⁺ and K⁺ over Ca\u0026sup2;⁺ may also reflect anthropogenic inputs, possibly linked to fertilizer use in agricultural activities and ion exchange processes in the aquifer matrix.\u003c/p\u003e\n \u003cp\u003eThe alignment of all groundwater samples within the rock dominance field in both diagrams, combined with their relatively high TDS, points to a dual influence of natural geochemical evolution through water\u0026ndash;rock interactions and external salinization, likely from seawater intrusion in coastal zones. Additionally, the moderate enrichment in sodium and chloride can also be influenced by agricultural runoff, including fertilizers and irrigation return flow (Manimaran et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.3. Chadha Diagram for Salinity Evolution Analysis\u003c/h2\u003e\n \u003cp\u003eThe Chadha diagram provides a powerful modification of the Piper diagram to simplify the identification of hydrochemical facies and the geochemical evolution processes in groundwater \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e (Guettaia et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBased on the results plotted for the 30 groundwater samples from the Skhirate coastal region, the majority of samples fall into the upper right quadrant (Field 4), which corresponds to a Na\u0026ndash;Cl type water. This indicates that groundwater chemistry in the region is strongly influenced by saline water mixing, likely due to seawater intrusion in the coastal aquifer (Thakur et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, a few samples are scattered into Field 3 (Ca\u0026ndash;Cl type), Field 1 (Ca\u0026ndash;HCO₃ type) and Field 2 (Na\u0026ndash;HCO₃ type), indicating freshwater recharge zones or areas where agricultural return flow or limited cation exchange may still play a role (Wen et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These points suggest localized areas of reduced salinity impact or hydrochemical buffering due to carbonate weathering. The dominance of calcium and sodium cations and chloride anions, as shown in the prevailing cluster of samples, reinforces the evidence of salinization due to seawater intrusion, especially in the zones closer to the coastline, where electrical conductivity (EC) and TDS levels were already high. This diagram also supports the findings of the Piper and Gibbs plots, confirming that the coastal aquifer is undergoing geochemical transformation from freshwater to saline-dominated facies, influenced by marine intrusion and evaporative concentration, alongside possible anthropogenic inputs from agricultural activities.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Evaluation of GW Suitability Using WQI, IWQI, and SMI\u003c/h2\u003e\n \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1. GW Suitability for Drinking Purposes\u003c/h2\u003e\n \u003cp\u003eThe Water Quality Index (WQI) serves as a crucial indicator for assessing the suitability of groundwater for human consumption (Barathkumar et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The analysis of WQI values across the sampled locations reveals considerable spatial variability in groundwater quality. The maximum WQI value was recorded in sample P13, reaching 251.86, which falls within the category of very poor water quality \u003cstrong\u003e(Fig.\u0026nbsp;9a)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThis high value suggests that the water at this location is highly impacted by contaminants and salinity and is not suitable for drinking without appropriate treatment. In contrast, the lowest WQI was observed in sample P17, with a value of 62.42, classifying it as good water quality. This sample indicates minimal contamination and suggests that the groundwater at this location may be safely used for drinking purposes, with minimal health risks. The mean WQI value across all 30 samples was calculated to be 133.48, placing the average water quality in the poor category. This suggests that, on average, the groundwater in the study area is compromised and does not meet the desirable standards for direct consumption without remediation. The high mean value may be attributed to elevated concentrations of parameters such as TDS, Cl-, Na\u0026thinsp;+\u0026thinsp;and NO3- in several sampling locations, resulting from agricultural runoff, leaching of fertilizers and saline water intrusion. In terms of distribution across WQI categories, the majority of the groundwater samples 76.67% were classified as having poor quality, indicating widespread degradation. Only 20% of the samples were considered to have good quality, and only one sample (P13) fell into the very poor quality category. This distribution underscores a pressing concern regarding the potability of groundwater in the area.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2. GW quality for irrigation applications\u003c/h2\u003e\n \u003cp\u003eThe assessment of groundwater quality for irrigation was performed using several indices including SAR, RSC, Na%, MAR, IP, RSBC, Kelly Index (KI), and Potential Salinity (PS) (Sanad et al., \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e). These indices help determine the suitability of water for long-term agricultural use and the possible impacts of salinity and sodicity hazards, especially under conditions influenced by seawater intrusion or poor agricultural practices. SAR values ranged from 1.47 (P1) to 7.71 (P13), with a mean of 4.05. Values below 10 are generally considered safe for irrigation \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCalculated irrigation water quality indices for groundwater samples from the Skhirate coastal region.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSamples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSAR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRSC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNa%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAR%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIP%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRSBC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-15.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIrrigation indices classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInterpretation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePermissible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25% \u0026ndash; 75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDoubtful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u0026ndash;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026ndash;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnsuitable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe majority of samples fall within the acceptable range, although elevated SAR values, particularly in P13 may indicate potential for soil permeability issues under long-term irrigation (Oueld Lhaj et al., \u003cspan class=\"CitationRef\"\u003e2025a\u003c/span\u003e). The USSL diagram is an important hydrochemical tool used to evaluate the suitability of GW for irrigation by plotting SAR against EC \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. These two factors are critical for assessing irrigation water quality as they influence soil permeability, structure, and crop productivity. In this study, all samples fall within the S1 class (low sodium hazard), indicating that the water is safe with respect to sodicity. However, variations in EC place the samples in different salinity hazard categories. The C3\u0026ndash;S1 (High salinity, low sodium) class includes the majority of samples (P1, P2, P3, P4, P6, P7, P19, P20, P25, P26, P27, P28, P29, and P30). While sodicity is not a problem, high salinity may pose risks to salt-sensitive crops.\u003c/p\u003e\n \u003cp\u003eIt requires moderate leaching and well-drained soils to avoid soil degradation. The C4\u0026ndash;S1 (Very high salinity, low sodium) category encompasses P8, P9, P10, P11, P12, P14, P15, P16, P17, P18, P21, P22, P23, and P24. These waters present very high salinity hazards, which could severely affect crop yields and soil quality unless significant leaching is practiced or salt-tolerant crops are cultivated. The agricultural utility of these waters is limited without adequate soil and crop management. Only P13 falls into the extreme class C5\u0026ndash;S1 (Extremely high salinity, low sodium). The groundwater at this location is unsuitable for irrigation under normal conditions due to the exceptionally high salinity. It may cause irreversible damage to soil structure and hinder plant growth without advanced irrigation techniques, such as drip irrigation with periodic flushing or blending with fresher water sources. Overall, while sodicity is not a concern for the groundwater samples analyzed, salinity is a significant issue for many samples. The dominance of C3\u0026ndash;S1 and C4\u0026ndash;S1 classes indicates the influence of natural mineral dissolution, evaporative concentration and seawater intrusion in shaping the groundwater chemistry. Sustainable irrigation practices and monitoring of soil salinity levels are recommended for long-term agricultural productivity in this region.\u003c/p\u003e\n \u003cp\u003eThe RSC exhibited a wide variation, from \u0026minus;\u0026thinsp;26.91 (P13) to 5.10 (P6), with a mean of -6.89. Negative RSC values suggest no hazard from carbonate and bicarbonate accumulation. However, a few positive values near or above 2.5 may indicate moderate to severe hazard, especially for P6. The Na%, a key indicator of sodicity, ranged between 17.62% (P1) and 52.81% (P5), with an average of 38.75%. Na% values above 40% are considered marginal to unsuitable, pointing to potential concerns with sodic water, especially in P5 and P4. This trend may suggest mixing with saline or seawater-affected sources. The Wilcox diagram, which plots Sodium Percentage (%Na) against Electrical Conductivity (EC), is a widely used tool to assess the suitability of groundwater for irrigation based on salinity and sodium hazard (Fentahun et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bhushan et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). It provides insight into how dissolved salts and sodium ions might affect soil structure, permeability, and crop yield. Based on the classification results from the Wilcox diagram for the 30 groundwater samples in the Skhirate coastal aquifer, three major water quality groups emerged \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e. These samples (P5, P10, P11, P12, P13, P16, P17, P21, P22, P23, and P24) exhibit high EC values combined with elevated %Na, placing them in the \u0026ldquo;doubtful\u0026rdquo; category. This classification implies that the use of such water for irrigation poses significant risks to soil permeability and crop health, especially for sodium-sensitive crops. The high salinity is likely a direct consequence of seawater intrusion, particularly in coastal zones. Additionally, the elevated %Na may be linked to intensive agricultural return flows, fertilizer overuse, and poor irrigation management. Long-term use of this water without remediation may result in sodification of the soil, reducing soil infiltration capacity and damaging soil structure. The samples (P1, P2, P3, P6, P20, P26, and P27) are situated in the \u0026ldquo;good to permissible\u0026rdquo; category, indicating that the groundwater is generally suitable for irrigation with minimal salinity or sodium hazard (Somay-Altas and Sanli, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). The relatively moderate EC and %Na values suggest lower influence from seawater intrusion and agricultural pollution, likely due to natural recharge or better aquifer protection in these areas. These zones might be prioritized for sustainable irrigation practices and groundwater conservation. This group of samples (P4, P7, P8, P9, P14, P15, P18, P19, P25, P28, P29, and P30) exhibits intermediate water quality, often representing transitional zones in the aquifer. The water may be permissible for irrigation but requires monitoring and soil management strategies, particularly for long-term agricultural use. These values may reflect a moderate degree of saline water intrusion or partial anthropogenic contamination. Best management practices (BMPs), such as leaching, crop rotation, and use of gypsum, may help mitigate potential negative effects in these areas (Nthebere et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sanad et al., \u003cspan class=\"CitationRef\"\u003e2025c\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe MAR values spanned from 26.19% (P16) to 60.35% (P20), averaging 43.28%. MAR values\u0026thinsp;\u0026gt;\u0026thinsp;50% can impair soil structure. A considerable number of samples (e.g., P20, P3, P15) exceeded this threshold, reinforcing concerns regarding long-term soil degradation due to excess magnesium ions.The PI showed values from 26.83% (P1) to 63.02% (P15), with a mean of 48.14%. While most samples fall in the moderate to good category, the variability indicates inconsistent water\u0026ndash;soil compatibility, likely related to varying salt compositions across the region. The RSBC values varied from \u0026minus;\u0026thinsp;16.61 (P13) to 5.16 (P6), with an average of -3.44. Samples with RSBC above 2.5 can negatively affect soil permeability. Again, P6 exhibits the highest RSBC value, suggesting a salinity and bicarbonate hazard. The KI ranged from 0.21 (P1) to 1.12 (P5). Values greater than 1 indicate unsuitability for irrigation. P5 stands out with a KI above the threshold, suggesting high sodium content relative to calcium and magnesium. The PS, which accounts for Cl⁻ and SO₄\u0026sup2;⁻ dominance, ranged from 8.42 (P2) to 54.34 (P16), with a mean of 22.56. Higher PS values can indicate salinization risks, particularly under evapotranspiration conditions. The elevated PS in P16 and P13 likely reflects saline intrusion or concentrated agricultural runoff. In summary, while several samples (e.g., P1, P2) demonstrate acceptable irrigation quality across most indices, others (notably P5, P6, P13, P16, and P20) exhibit elevated values in one or more indices that suggest potential degradation due to seawater intrusion, bicarbonate imbalance, and intensive fertilizer use. These results emphasize the need for integrated water management strategies and regular monitoring to mitigate soil salinization risks in coastal and agriculturally active areas.\u003c/p\u003e\n \u003cp\u003eThe IWQI values for the 30 groundwater samples in the Skhirate coastal region reflect diverse levels of irrigation suitability, shaped by both seawater intrusion and intensive agricultural activities. The IWQI values ranged from 53.47 in sample P1 to 219.62 in sample P13, with a mean value of 114.37 \u003cstrong\u003e(Fig.\u0026nbsp;9b)\u003c/strong\u003e. The lowest value, observed in P1, reflects good irrigation water quality, indicative of minimal salinity and sodium hazard. On the other hand, the highest value in P13 suggests very poor irrigation suitability, likely resulting from excessive levels of salinity-related parameters such as EC, Na⁺, and Cl⁻. Notably, sample P16 also recorded an IWQI above 200, categorizing it as very poor, and supporting evidence of salinization from seawater intrusion. Based on IWQI classification thresholds, 13 samples (43.33%) fall within the range 50\u0026ndash;100, indicating good quality water for irrigation. While 15 samples (50%) fall between 100 and 200, corresponding to poor irrigation quality and 2 samples (P13 and P16) have values between 200\u0026ndash;300, classifying them as very poor and unsuitable for irrigation. This classification demonstrates that although a notable proportion of groundwater is suitable for irrigation, over half the samples exhibit limitations, with the poor and very poor classes linked to salt accumulation and high sodium content. These conditions are exacerbated by coastal proximity, where seawater intrusion, coupled with agricultural runoff, likely contributes to deteriorating water quality.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.3. Assessment of Seawater Intrusion Using the SMI\u003c/h2\u003e\n \u003cp\u003eThe SMI serves as a powerful diagnostic tool for evaluating the degree of seawater intrusion in coastal aquifers (Goswami and Rai, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). By integrating the relative concentrations of four conservative ions Na⁺, Mg\u0026sup2;⁺, Cl⁻, and SO₄\u0026sup2;⁻, the SMI provides a dimensionless index that reflects the chemical mixing of marine and freshwater systems. In this study, SMI values ranged from a minimum of 0.53 (sample P1) to a maximum of 3.52 (sample P13), with a mean value of 1.37 \u003cstrong\u003e(Fig.\u0026nbsp;9c)\u003c/strong\u003e. The lowest SMI value, observed in P1, suggests minimal influence from seawater, indicating a more preserved hydrochemical composition of inland or better-protected groundwater. In contrast, P13 exhibits the highest SMI, strongly pointing to intense mixing with saline water, likely due to proximity to the coastline, over-pumping of aquifers, or lateral seawater intrusion. The high mean SMI value (\u0026gt;\u0026thinsp;1) further emphasizes the systemic vulnerability of the aquifer to salinization processes. The classification of samples based on the SMI threshold of 1 reveals, 12 samples (40%) have SMI\u0026thinsp;\u0026lt;\u0026thinsp;1.0, indicating groundwater with minimal to moderate seawater impact and 18 samples (60%) exhibit SMI\u0026thinsp;\u0026gt;\u0026thinsp;1.0, signifying significant salinity levels attributable to seawater intrusion. The majority of affected samples are geographically concentrated closer to the coast or in areas of intensive irrigation, where high extraction rates may induce seawater ingress. Moreover, the elevated concentrations of Na⁺ and Cl⁻ in these samples corroborate the dominance of marine-derived salinity (Liu et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings align with previous geochemical assessments and support the notion that both natural and anthropogenic forces, particularly unregulated pumping and fertilizer leaching contribute to the deterioration of groundwater quality.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Integration of AI and ML Models for Predicting Groundwater Quality Indices\u003c/h2\u003e\n \u003cp\u003eTo enhance the understanding of groundwater quality dynamics and facilitate spatially informed decision-making, ML techniques were applied to predict three key groundwater quality indices including WQI, IWQI and SMI. Random Forest (RF) and Artificial Neural Networks (ANN) were employed for predictive modeling (Philip and Nidhi, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These models were trained using comprehensive physico-chemical parameters of groundwater samples collected from the coastal region of Skhirate, Morocco.\u003c/p\u003e\n \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1. Prediction of Water Quality Indices (WQI, IWQI and SMI)\u003c/h2\u003e\n \u003cp\u003eThe Random Forest model achieved a moderate predictive performance for WQI with an R\u0026sup2; value of 0.47, RMSE of 18.87, and MAE of 17.88 \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003ea\u003cstrong\u003e)\u003c/strong\u003e. The ANN model showed lower predictive ability for WQI, with R\u0026sup2; = 0.29, indicating that Random Forest was better suited for WQI modeling in this study area. The predicted vs. actual scatter plot for the RF model revealed acceptable clustering along the diagonal line, suggesting adequate model fitting despite limited sample size (n\u0026thinsp;=\u0026thinsp;30) \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003eb\u003cstrong\u003e)\u003c/strong\u003e. For IWQI, the ANN model significantly outperformed Random Forest. ANN achieved a high R\u0026sup2; value of 0.81, with RMSE\u0026thinsp;=\u0026thinsp;18.04 and MAE\u0026thinsp;=\u0026thinsp;14.03, compared to RF\u0026rsquo;s R\u0026sup2; = 0.55.\u003c/p\u003e\n \u003cp\u003eThis suggests that ANN effectively captured complex nonlinear relationships between input parameters and IWQI, highlighting its suitability for irrigation quality assessment using integrated physico-chemical datasets. In contrast, the Random Forest model outperformed ANN in predicting SMI. RF attained a high R\u0026sup2; score of 0.74, with RMSE\u0026thinsp;=\u0026thinsp;0.39 and MAE\u0026thinsp;=\u0026thinsp;0.26. Meanwhile, ANN achieved R\u0026sup2; = 0.50, indicating moderate performance. These results reflect that the RF model more effectively captures the drivers of saltwater intrusion through multiple geochemical indicators. Overall, the results affirm the utility of ML models in hydrogeochemical studies for predictive purposes. Random Forest demonstrated superior predictive capacity for WQI and SMI, while ANN excelled in IWQI modeling. The adoption of ML enhances traditional hydrochemical assessments by facilitating early detection of deteriorating water quality and supporting decision-making processes for sustainable groundwater management (Rajeev et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2. Spatial Prediction and Distribution of Groundwater Quality Indices Using AI Models\u003c/h2\u003e\n \u003cp\u003eThe spatial distribution maps of predicted WQI, IWQI, and SMI were generated using machine learning-based predictions and georeferenced using ArcGIS software. These maps provide critical insights into the spatial variability of groundwater quality across the study area \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eThe spatial pattern of predicted WQI values, ranging from 53.78 to 219.96, reveals a pronounced zonal gradient. The lowest WQI values were recorded in the southeastern part of the study area (samples P1 to P3), indicating better water quality for general use. In contrast, the highest WQI values were observed in the northwestern to central-western regions, particularly near sampling points P11, P12, P13, and P16. This cluster of elevated WQI may be linked to anthropogenic influences such as intensive agriculture, wastewater recharge, or seawater mixing (Sanad et al., \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e). The northwestern proximity to the coast further supports the influence of marine intrusion and surface runoff on groundwater degradation. The predicted IWQI values varied between 83.27 and 219.16, with a spatial pattern that closely mirrors that of WQI. Higher IWQI values, indicating poorer irrigation suitability, are again concentrated in the northwestern and central zones, corresponding to samples such as P11, P12, P23, and P24. These areas likely face elevated salinity or ion imbalances that are detrimental to sensitive crops. Conversely, the lowest IWQI values indicative of relatively suitable irrigation water, were predominantly located in the southeastern zone (samples P1 to P3), where minimal salt accumulation and geochemical contamination are evident. The spatial interpolation of SMI predictions range between 0.59 and 3.12 offers a more direct representation of seawater intrusion potential. The highest SMI values, pointing to greater influence of marine water mixing, were primarily observed in the central region, notably around sampling points P20 and P25. In contrast, the eastern and southeastern zones, such as around P1 to P3 and P29 to P30, displayed the lowest SMI values, reinforcing the interpretation that these zones are less affected by seawater intrusion. This pattern aligns well with the hydrogeological gradient and topographical drainage trends that may impede inland saltwater migration in those regions.\u003c/p\u003e\n \u003cp\u003eThese spatial insights are vital for sustainable groundwater management. The northwestern and central areas, consistently demonstrating high values for all three indices (WQI, IWQI, SMI), are deemed zones of concern requiring urgent monitoring and mitigation interventions. Meanwhile, the southeastern groundwater zones maintain better quality, suitable for both domestic and farming use. The successful integration of AI predictions and GIS-based spatial analysis confirms the value of intelligent modeling approaches in hydro-environmental assessments and supports future predictive and planning frameworks.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Groundwater Salinization Impacts on Soil Health and Crop Productivity: Soil\u0026ndash;Water\u0026ndash;Plant Interactions in Coastal Agroecosystems\u003c/h2\u003e\n \u003cp\u003eThe rise of GW salinity, particularly in coastal agricultural regions such as Skhirat, presents a profound risk to soil functionality and crop performance. Our findings, supported by WQI, IWQI, and SMI indices, reveal that more than 50% of the sampled wells exhibit poor to very poor irrigation suitability, with SMI\u0026thinsp;\u0026gt;\u0026thinsp;1 in 60% of samples, indicating active seawater intrusion processes. Such GW degradation not only affects water availability but also disrupts the delicate soil\u0026ndash;plant\u0026ndash;water continuum, leading to cascading agronomic consequences. In saline conditions, osmotic stress is the primary physiological constraint that plants encounter (Zafar et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). High concentrations of Na⁺, Cl⁻ and other salts in irrigation water lower the soil water potential, reducing the ability of roots to absorb water, even when soil moisture appears adequate (Shqerat and Al-Tabbal, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This \u0026ldquo;physiological drought\u0026rdquo; effect leads to stunted growth, reduced leaf expansion, and impaired stomatal regulation, ultimately lowering photosynthetic efficiency (Du et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, ion-specific toxicity especially from Na⁺ and Cl⁻ disrupts intracellular ionic homeostasis. Excess Na⁺ interferes with K⁺ uptake, a critical nutrient for enzyme activation and stomatal functioning, while Cl⁻ accumulation can lead to leaf chlorosis and necrosis, particularly in sensitive crops such as tomato, citrus, and lettuce (Kharwar et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). As demonstrated in our study, high SAR values (\u0026gt;\u0026thinsp;10 in multiple wells) suggest strong sodicity risks, which impair plant nutrient uptake and water transport mechanisms.\u003c/p\u003e\n \u003cp\u003eFrom a soil perspective, the prolonged use of saline GW triggers profound changes in soil structure and microbial dynamics. Elevated sodium levels lead to cation exchange imbalances, displacing Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ from the soil complex (Regasa et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). This results in soil dispersion, collapse of macro-aggregates, and decreased porosity ultimately reducing infiltration rates, aeration, and root penetration (Mondal et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These structural changes are especially pronounced in fine-textured alluvial soils, which dominate the Skhirat coastal plain. The microbial ecosystem in saline-affected soils is also altered. High salt concentrations reduce microbial biomass, affect enzyme activities (e.g., dehydrogenase, phosphatase), and shift microbial community composition toward more salt-tolerant taxa (Manhou et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sanad et al., \u003cspan class=\"CitationRef\"\u003e2025a\u003c/span\u003e). This decline in microbial diversity and activity hampers essential soil processes, such as organic matter mineralization and nitrogen cycling, thereby impairing long-term soil fertility. The interaction between saline irrigation and shallow water tables further compounds the problem. In regions where evapotranspiration exceeds precipitation, upward capillary movement brings dissolved salts to the root zone and soil surface, leading to secondary salinization. Crops exposed to this dual threat saline water and saline soils experience compounded physiological stress, especially during germination and early vegetative stages. Furthermore, yield reductions in saline environments can range between 10\u0026ndash;50% depending on crop species, soil texture, and irrigation management (El-Ramady et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These yield penalties are associated not only with reduced water uptake but also with nutrient imbalances, including deficiencies in nitrogen, phosphorus, and potassium, further limiting plant metabolism and reproductive success.\u003c/p\u003e\n \u003cp\u003eTherefore, the combined evidence from salinity indices, hydrogeochemical diagrams, and spatial AI-based predictions confirms a pressing need to monitor and manage GW quality in coastal agricultural systems. The intersection of degraded water quality, fragile soil health, and sensitive crop systems demands a multidimensional strategy that integrates soil\u0026ndash;water\u0026ndash;plant feedbacks, adapts crop choices, and deploys technological tools for sustainability under evolving climatic and anthropogenic pressures (Djibril et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tiabou et al., \u003cspan class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Suh et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tiabou et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Recommendations and Mitigation Strategies for Coastal Agroecosystems\u003c/h2\u003e\n \u003cp\u003eTo mitigate the adverse impacts of GW salinization on agricultural sustainability, a suite of integrated strategies must be adopted at the watershed and farm scale. First, improving irrigation efficiency through technologies such as drip and subsurface irrigation can reduce water loss, minimize salt accumulation, and limit groundwater overexploitation. Coupled with soil moisture sensors and scheduling systems, precision irrigation can optimize water use while maintaining crop yield (Seyar and Ahamed, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, conjunctive use of surface and GW resources, including the blending of freshwater with moderately saline GW, can help dilute salinity levels, especially during critical crop growth stages. In areas where freshwater is scarce, treated wastewater reuse offers an alternative source of irrigation, provided it meets quality standards. Third, the cultivation of salt-tolerant crops and cultivars (e.g., quinoa, barley, and halophyte vegetables) can maintain farm productivity under increasing salinity stress (Bazihizina et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). These crops exhibit physiological traits such as osmotic adjustment and selective ion transport, allowing them to survive and yield in saline environments. Additionally, organic soil amendments, such as compost, biochar, and green manure, enhance soil buffering capacity, improve structure, and promote microbial activity, all of which are crucial for rehabilitating salt-affected soils (Oueld Lhaj et al., \u003cspan class=\"CitationRef\"\u003e2024a\u003c/span\u003e, 2025b, \u003cspan class=\"CitationRef\"\u003e2025a\u003c/span\u003e). At the aquifer scale, managed aquifer recharge (MAR) using excess runoff or treated wastewater can dilute saline zones and restore hydraulic balance (Achilleos et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Monitoring systems supported by AI-based early warning tools should be established to detect salinity thresholds in real-time, enabling proactive management (Miller et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Finally, strengthening water governance, enforcing extraction quotas, and raising farmer awareness through extension services will be critical to achieving sustainable groundwater management in coastal zones threatened by seawater intrusion.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study integrated hydrochemical analysis and artificial intelligence-based modeling to evaluate groundwater quality in a coastal agricultural zone under increasing salinization pressure. By calculating and mapping key water quality indices namely the WQI, IWQI and SMI, the research provided a comprehensive spatial assessment of groundwater conditions using RF, ANN and GIS-based interpolation.\u003c/p\u003e\n\u003cp\u003eThe results revealed significant spatial variability in groundwater quality, with pronounced degradation toward the western and northwestern zones, adjacent to the Atlantic coast. These areas showed elevated WQI and SMI values, highlighting the influence of seawater intrusion and unsustainable agricultural practices. The predictive models effectively captured these spatial trends, reinforcing the utility of AI in anticipating groundwater degradation. Seawater intrusion not only increases salinity levels but also disrupts the geochemical balance of soils. In coastal agroecosystems, this leads to reduced soil permeability, impaired nutrient uptake, and ultimately, a decline in crop productivity. Elevated sodium concentrations, evident in SAR and %Na values contribute to clay swelling, soil structure deterioration, and poor infiltration. The progressive salinization trend observed in this study aligns with the regional hydrogeological gradient and may be exacerbated by over-extraction and ineffective irrigation management.\u003c/p\u003e\n\u003cp\u003eTo mitigate the risks of groundwater salinization and safeguard agricultural productivity, several strategies are recommended. These include adopting precision irrigation techniques such as drip irrigation to limit salt accumulation, blending freshwater with brackish sources for optimized irrigation, and cultivating salt-tolerant crops like quinoa and barley. Furthermore, managed aquifer recharge (MAR) and AI-assisted early warning systems should be integrated into regional groundwater monitoring frameworks to ensure sustainable resource management in coastal areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHatim Sanad:\u003c/strong\u003e Conceptualization, Methodology, Software, Resources, Validation, Formal analysis, Writing—original draft, Writing – review and editing, Visualization. \u003cstrong\u003eRachid Moussadek:\u003c/strong\u003e Formal analysis, Writing – review and editing, Funding acquisition. \u003cstrong\u003eAbdelmjid Zouahri:\u003c/strong\u003e Methodology, Validation, Writing—original draft, Writing – review and editing, Supervision. \u003cstrong\u003eMajda Oueld Lhaj:\u003c/strong\u003e Conceptualization, Methodology, Writing—original draft, Writing – review and editing. \u003cstrong\u003eLatifa Mouhir:\u0026nbsp;\u003c/strong\u003eValidation, Writing—original draft, Writing – review and editing, Supervision. \u003cstrong\u003eHouria Dakak:\u003c/strong\u003e Validation, Writing—original draft, Writing – review and editing, supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all collaborators who participated in field sampling, laboratory analyses, and manuscript preparation. The authors are also grateful to the MCGP INRA-ICARDA and EiA projrots for their financial support. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcharki, S., Raza, A., Vishwakarma, D.K., Amharref, M., Bernoussi, A.S., Singh, S.K., Al-Ansari, N., Dewidar, A.Z., Al-Othman, A.A., Mattar, M.A., 2025. Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates. Scientific Reports 15, 2542.\u003c/li\u003e\n \u003cli\u003eAchilleos, M., Tzoraki, O., Akylas, E., 2025. Managed Aquifer Recharge (MAR) in Semiarid Regions: Water Quality Evaluation and Dynamics from the Akrotiri MAR System, Cyprus. Hydrology 12. https://doi.org/10.3390/hydrology12050123\u003c/li\u003e\n \u003cli\u003eBala Dhandayuthapani, V., 2024. Python Data Analysis and Visualization in Java GUI Applications Through TCP Socket Programming. International Journal of Information Technology and Computer Science 16, 72\u0026ndash;92.\u003c/li\u003e\n \u003cli\u003eBarathkumar, S., Sellamuthu, K.M., Sathiyabama, K., Malathi, P., Kumaraperumal, R., Devagi, P., Pazhanivelan, S., 2025. Groundwater quality assessment in selected polluted hotspots of Tamil Nadu, India using geospatial and statistical approaches. Desalination and Water Treatment 322, 101047. https://doi.org/10.1016/j.dwt.2025.101047\u003c/li\u003e\n \u003cli\u003eBazihizina, N., Papenbrock, J., Aronsson, H., Ben Hamed, K., Elmaz, \u0026Ouml;., Dafku, Z., Cust\u0026oacute;dio, L., Rodrigues, M.J., Atzori, G., Negacz, K., 2024. The Sustainable Use of Halophytes in Salt-Affected Land: State-of-the-Art and Next Steps in a Saltier World. Plants 13. https://doi.org/10.3390/plants13162322\u003c/li\u003e\n \u003cli\u003eBhushan, C., Patel, A.P., Kumar, A., Pandey, V.K., Kumar, P.V., Kumar, P., Tiwari, A.K., 2025. Hydrogeochemical characterisation and human health risk assessment of groundwater in Sultanpur District, Uttar Pradesh, India. Frontiers in Water Volume 7-2025. https://doi.org/10.3389/frwa.2025.1639708\u003c/li\u003e\n \u003cli\u003eBoutahri, Y., Tilioua, A., 2024. Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings. Results in Engineering 22, 102148. https://doi.org/10.1016/j.rineng.2024.102148\u003c/li\u003e\n \u003cli\u003eBrhane, G.K., Mekonen, H.S., 2024. Identifying controlling factors for the evolution of shallow groundwater chemistry of Ellala catchment, northern Ethiopia. Sustainable Water Resources Management 10, 62.\u003c/li\u003e\n \u003cli\u003eChamoli, A., Bhambri, A., Karn, S.K., Raj, V., 2024. Ammonia, nitrite transformations and their fixation by different biological and chemical agents. Chemistry and Ecology 40, 166\u0026ndash;199. https://doi.org/10.1080/02757540.2023.2300780\u003c/li\u003e\n \u003cli\u003eChandrajith, R., Bandara, U.G.C., Diyabalanage, S., Senaratne, S., Barth, J.A.C., 2022. Application of Water Quality Index as a vulnerability indicator to determine seawater intrusion in unconsolidated sedimentary aquifers in a tropical coastal region of Sri Lanka. Groundwater for Sustainable Development 19, 100831. https://doi.org/10.1016/j.gsd.2022.100831\u003c/li\u003e\n \u003cli\u003eChen, W., Ma, X., Wang, Z., Li, W., Fan, C., Zhang, J., Que, X., Li, C., 2024. Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction. Earth Science Informatics 17, 1819\u0026ndash;1835.\u003c/li\u003e\n \u003cli\u003eChrif El Idrissi, M., Saber, E.-R., Al Mashoudi, A., 2025. Hydro-climatic stress and ecosystem vulnerability in a highland lake: the case of Dayet Aoua (1935\u0026ndash;2022), Middle Atlas, Morocco. Modeling Earth Systems and Environment 11, 312. https://doi.org/10.1007/s40808-025-02496-8\u003c/li\u003e\n \u003cli\u003eDai, R., Hou, Y., Li, X., 2025. The optimization strategies for crop nutrient management in saline-alkali soil environments: From traditional farming techniques to the integrated application of smart agriculture. Advances in Resources Research 5, 1552\u0026ndash;1570.\u003c/li\u003e\n \u003cli\u003eDjibril, K.N.G., Yiika, L.P., Etutu, M.E.M.M., Eric, B.E., Mengu, E.E., Jean-Lavenir, N.M., Armstrong-Altrin, J.S., 2024. Geochemistry of sediments from the Mugheb River, Bamenda, Cameroon Volcanic Line: implications for provenance, paleoweathering and tectonic setting. Arabian Journal of Geosciences 17, 331. https://doi.org/10.1007/s12517-024-12148-3\u003c/li\u003e\n \u003cli\u003eDu, X., Du, Y., Feng, N., Zheng, D., Zhou, H., Huo, J., 2024. Exogenous Uniconazole promotes physiological metabolism and grain yield of rice under salt stress. Frontiers in Plant Science Volume 15-2024. https://doi.org/10.3389/fpls.2024.1459121\u003c/li\u003e\n \u003cli\u003eDzulhijjah, D.A., Kusrini, ., Martinez-Bejar, R., 2025. Comparative Analysis of Machine Learning Models for Sentinel-2 based Classification of the Bornean Heath Forest. Eng. Technol. Appl. Sci. Res. 15, 21937\u0026ndash;21943. https://doi.org/10.48084/etasr.10173\u003c/li\u003e\n \u003cli\u003eEdet, A., 2017. Hydrogeology and groundwater evaluation of a shallow coastal aquifer, southern Akwa Ibom State (Nigeria). Applied Water Science 7, 2397\u0026ndash;2412. https://doi.org/10.1007/s13201-016-0432-1\u003c/li\u003e\n \u003cli\u003eEl Hammioui, Y., Belghiti, M.L., Boutagayout, A., Hili, A., El Boute, S., Hilali, A., Azzirgue, E.M., Abba, E.H., 2025. Assessing the groundwater quality in the Khenifra Province, Morocco. Mediterranean Geoscience Reviews 7, 119\u0026ndash;137. https://doi.org/10.1007/s42990-024-00141-7\u003c/li\u003e\n \u003cli\u003eEl-Naggar, M.M., Fishar, M.R., Khalil, M.T., 2025. Climatic Variation and the Aquatic Biodiversity in Egypt, in: Climate Changes Impacts on Aquatic Environment: Assessment, Adaptation, Mitigation, and Road Map for Sustainable Development. Springer, pp. 65\u0026ndash;84.\u003c/li\u003e\n \u003cli\u003eEl-Ramady, H., Prokisch, J., Mansour, H., Bayoumi, Y.A., Shalaby, T.A., Veres, S., Brevik, E.C., 2024. Review of Crop Response to Soil Salinity Stress: Possible Approaches from Leaching to Nano-Management. Soil Systems 8. https://doi.org/10.3390/soilsystems8010011\u003c/li\u003e\n \u003cli\u003eFentahun, A., Mechal, A., Karuppannan, S., 2023. Hydrochemistry and quality appraisal of groundwater in Birr River Catchment, Central Blue Nile River Basin, using multivariate techniques and water quality indices. Environmental Monitoring and Assessment 195, 655. https://doi.org/10.1007/s10661-023-11198-6\u003c/li\u003e\n \u003cli\u003eGad, M., Gaagai, A., Agrama, A.A., El-Fiqy, W.F., Eid, M.H., Szűcs, P., Elsayed, S., Elsherbiny, O., Khadr, M., Abukhadra, M.R., 2024. Comprehensive evaluation and prediction of groundwater quality and risk indices using quantitative approaches, multivariate analysis, and machine learning models: An exploratory study. Heliyon 10.\u003c/li\u003e\n \u003cli\u003eGoswami, S., Rai, A.K., 2024. Identifying intrusion of seawater in coastal aquifers by modified GALDIT (M-GALDIT) index. Groundwater for Sustainable Development 25, 101173. https://doi.org/10.1016/j.gsd.2024.101173\u003c/li\u003e\n \u003cli\u003eGrandika, D.S., Nuha, H.H., Putrada, A.G., 2024. Predicting Soil Moisture Levels with Long Short-Term Memory: A Deep Learning Approach Integrated with Internet of Things Data, in: 2024 International Conference on Decision Aid Sciences and Applications (DASA). pp. 1\u0026ndash;6. https://doi.org/10.1109/DASA63652.2024.10836388\u003c/li\u003e\n \u003cli\u003eGuettaia, S., Boudjema, A., Derdour, A., Laoufi, A., Almohamad, H., Al-Mutiry, M., Abdo, H.G., 2025. Hydrochemical Characterization and Predictive Modeling of Groundwater Quality in Karst Aquifers Under Semi-Arid Climate: A Case Study of Ghar Boumaaza, Algeria. Sustainability 17. https://doi.org/10.3390/su17156883\u003c/li\u003e\n \u003cli\u003eHe, Q., Xu, Y., Jin, Z., Hu, S., Pan, J., Zhao, P., Chi, R., 2025. Effect of sodium citrate on the expansion of clay minerals during leaching process of ionic rare-earth ores. Colloids and Surfaces A: Physicochemical and Engineering Aspects 137869.\u003c/li\u003e\n \u003cli\u003eHuang, M., Qi, J., Zhang, C., Wang, Y., Chen, Y., Shao, J., Wu, S., 2025. Spatiotemporal Analysis of Sea-Surface pH in the Pacific Ocean Based on Interpretable Machine Learning. Journal of Marine Science and Engineering 13. https://doi.org/10.3390/jmse13071220\u003c/li\u003e\n \u003cli\u003eIgwebuike, N., Ajayi, M., Okolie, C., Kanyerere, T., Halihan, T., 2025. Application of machine learning and deep learning for predicting groundwater levels in the West Coast Aquifer System, South Africa. Earth Science Informatics 18, 6.\u003c/li\u003e\n \u003cli\u003eIqbal, J., Su, C., Ahmad, M., Baloch, M.Y.J., Rashid, A., Ullah, Z., Abbas, H., Nigar, A., Ali, A., Ullah, A., 2024. Hydrogeochemistry and prediction of arsenic contamination in groundwater of Vehari, Pakistan: comparison of artificial neural network, random forest and logistic regression models. Environmental Geochemistry and Health 46, 14.\u003c/li\u003e\n \u003cli\u003eIsık, H., Akkan, T., 2025. Water quality assessment with artificial neural network models: Performance comparison between SMN, MLP and PS-ANN methodologies. Arabian Journal for Science and Engineering 50, 369\u0026ndash;387.\u003c/li\u003e\n \u003cli\u003eIsmail, M., Pradhanang, S.M., Boving, T., Motta, S., McCarron, B., Volk, A., 2024. Review of modeling approaches at the freshwater and saltwater interface in coastal aquifers. Land 13, 1332.\u003c/li\u003e\n \u003cli\u003eJ, A., G, N., Sureshkumar, C., V, N., Kadry, S., 2024. Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions. Procedia Computer Science 235, 2\u0026ndash;11. https://doi.org/10.1016/j.procs.2024.04.001\u003c/li\u003e\n \u003cli\u003eJahan, S., Manzur, I., Shahria Alam, B.M., Rahman, T., Momo, I.J., Kibria, G., Yousuf Ali, Md.N., 2025. Optimizing Crop Selection Through Hyperparameter Tuning of Neural Networks: Development and Evaluation of Deep Learning Models for Enhancing Crop Recommendation Systems, in: 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT). pp. 276\u0026ndash;282. https://doi.org/10.1109/CSNT64827.2025.10968031\u003c/li\u003e\n \u003cli\u003eJodhani, K.H., Gupta, N., Dadia, S., Patel, H., Patel, D., Jamjareegulgarn, P., Singh, S.K., Rathnayake, U., 2025. Sustainable groundwater management through water quality index and geochemical insights in Valsad India. Scientific Reports 15, 8769. https://doi.org/10.1038/s41598-025-92053-1\u003c/li\u003e\n \u003cli\u003eKaiser, H.F., 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187\u0026ndash;200. https://doi.org/10.1007/BF02289233\u003c/li\u003e\n \u003cli\u003eKharwar, R.N., Prajapati, P., Sinha, S., 2025. Endophytic fungi mediated alleviation of salinity stress in crop plants: an insight and future prospect. Symbiosis 96, 1\u0026ndash;28. https://doi.org/10.1007/s13199-025-01060-3\u003c/li\u003e\n \u003cli\u003eKong, L., Chu, H., Hou, G., Zeng, J., Zhao, L., Han, X., Li, Y., Zhou, F., Yin, M., Zhang, X., 2025. Characteristics and mechanisms of sea (salt) water intrusion in Laizhou City, Shandong Province, China. Frontiers in Marine Science Volume 12-2025. https://doi.org/10.3389/fmars.2025.1578175\u003c/li\u003e\n \u003cli\u003eKumar, S., Agarwal, M., Deshpande, V., Cooper, J.R., Khosravi, K., Rathnayake, N., Hoshino, Y., Kantamaneni, K., Rathnayake, U., 2024. AI-driven predictions of geophysical river flows with vegetation. Scientific reports 14, 16368.\u003c/li\u003e\n \u003cli\u003eLeena Sri, R., Vetriveeran, D., Sambandam, R.K., Jenefa, J., Thangavel, K., 2025. Transformative Trends in AI for Environmental Monitoring: Challenges, Applications. Environmental Monitoring Using Artificial Intelligence 1\u0026ndash;28.\u003c/li\u003e\n \u003cli\u003eLiu, S.-S., Bai, Y.-N., Ni, J., He, Z., Yang, G.-P., 2025. Sources and Emissions of Marine-Derived Halocarbons Over the Coastal and Open Ocean of the Northwest Pacific. Journal of Geophysical Research: Atmospheres 130, e2024JD042706. https://doi.org/10.1029/2024JD042706\u003c/li\u003e\n \u003cli\u003eLo Medico, F., Rizzo, P., Rotigliano, E., Celico, F., 2025. Groundwater Contamination: Study on the Distribution and Mobility of Metals and Metalloids in Soil and Rocks. International Journal of Environmental Research and Public Health 22. https://doi.org/10.3390/ijerph22020182\u003c/li\u003e\n \u003cli\u003elotfinasabasl, S., Gohardoust, A., Dargahian, F., Zandifar, S., 2025. Comprehensive evaluation of groundwater quality and drought susceptibility in Jazmurian Basin Iran using integrated statistical GIS analysis. Scientific Reports 15, 11007. https://doi.org/10.1038/s41598-025-95839-5\u003c/li\u003e\n \u003cli\u003eMalal, H., Garcia, J.A., Marrs, A., Ait Hamza, M., Emerson, C., Nocco, M., Lakhtar, H., Lazcano, C., 2025. Organic and inorganic fertilizers modulate the response of the soil microbiome to salinity stress. Frontiers in Microbiology 16, 1551586.\u003c/li\u003e\n \u003cli\u003eMalik, A., Yasar, A., Tabinda, A., Iftikhar, M., Mukhtar, S., 2024. Health risk assessment of nitrates and fluorides ingestion and geochemical evaluation of groundwater characteristics in semi-arid regions. International Journal of Environmental Science and Technology 21, 8459\u0026ndash;8486.\u003c/li\u003e\n \u003cli\u003eManhou, K., Moussadek, R., Yachou, H., Zouahri, A., Douaik, A., Hilal, I., Ghanimi, A., Hmouni, D., Dakak, H., 2024. Assessing the Impact of Saline Irrigation Water on Durum Wheat (cv. Faraj) Grown on Sandy and Clay Soils. Agronomy 14. https://doi.org/10.3390/agronomy14122865\u003c/li\u003e\n \u003cli\u003eManhou, K., Taghouti, M., Moussadek, R., Elyacoubi, H., Bennani, S., Zouahri, A., Ghanimi, A., Sanad, H., Oueld Lhaj, M., Hmouni, D., Dakak, H., 2025. Performance, Agro-Morphological, and Quality Traits of Durum Wheat (Triticum turgidum L. ssp. durum Desf.) Germplasm: A Case Study in Jem\u0026acirc;a Sha\u0026iuml;m, Morocco. Plants 14. https://doi.org/10.3390/plants14101508\u003c/li\u003e\n \u003cli\u003eManimaran, D., Abishek, S.R., Pitchaimani, V.S., Ravindran, A.A., Soniyamary, M., Karuppannan, S., 2025. Spatial variation and hydrogeochemical characterization of groundwater in Vallanadu region: insights from water quality index and statistical analysis. Discover Sustainability 6, 524. https://doi.org/10.1007/s43621-025-01381-9\u003c/li\u003e\n \u003cli\u003eMiller, T., Durlik, I., Kostecka, E., Kozlovska, P., Łobodzińska, A., Sokołowska, S., Nowy, A., 2025. Integrating Artificial Intelligence Agents with the Internet of Things for Enhanced Environmental Monitoring: Applications in Water Quality and Climate Data. Electronics 14. https://doi.org/10.3390/electronics14040696\u003c/li\u003e\n \u003cli\u003eMondal, S., Saha, S., Das, S.R., Chatterjee, D., 2024. Impact of Conservation Agriculture on Soil Health and Environmental Sustainability, in: Pathak, H., Chatterjee, D., Saha, S., Das, B. (Eds.), Climate Change Impacts on Soil-Plant-Atmosphere Continuum. Springer Nature Singapore, Singapore, pp. 255\u0026ndash;281. https://doi.org/10.1007/978-981-99-7935-6_10\u003c/li\u003e\n \u003cli\u003eNthebere, K., Apoorva, M.R., Chandra, M.S., Yadav, M.B.N., Prakash, T.R., 2023. Farming Technologies and Carbon Sequestration Alternatives to Combat Climate Change Through Mitigation of Greenhouse Gas Emissions, in: Chatterjee, U., Shaw, R., Kumar, S., Raj, A.D., Das, S. (Eds.), Climate Crisis: Adaptive Approaches and Sustainability. Springer Nature Switzerland, Cham, pp. 253\u0026ndash;275. https://doi.org/10.1007/978-3-031-44397-8_14\u003c/li\u003e\n \u003cli\u003eOjo, O.T., Ike, C.J., Aladeboyeje, A.I., Olaseeni, O.G., Adewumi, O.F., 2025. Hydrochemical assessment of borehole water in Effurun, Delta state Niger Delta, Nigeria: implications for public health and safety. Discover Geoscience 3, 10. https://doi.org/10.1007/s44288-025-00117-6\u003c/li\u003e\n \u003cli\u003eOkebalama, C.B., Udeani, C.M., Awaogu, C.E., 2024. Aggregate Stability And The Influence Of Sodium Chloride On Dispersion Of Sandy Clay Loam Soils In Southeastern Nigeria. Global Journal of Agricultural Sciences 23, 49\u0026ndash;60.\u003c/li\u003e\n \u003cli\u003eOueld Lhaj, M., Moussadek, R., Mouhir, L., Mdarhri Alaoui, M., Sanad, H., Iben Halima, O., Zouahri, A., 2024a. Assessing the Evolution of Stability and Maturity in Co-Composting Sheep Manure with Green Waste Using Physico-Chemical and Biological Properties and Statistical Analyses: A Case Study of Botanique Garden in Rabat, Morocco. Agronomy 14. https://doi.org/10.3390/agronomy14071573\u003c/li\u003e\n \u003cli\u003eOueld Lhaj, M., Moussadek, R., Mouhir, L., Sanad, H., Manhou, K., Iben Halima, O., Yachou, H., Zouahri, A., Mdarhri Alaoui, M., 2025a. Application of Compost as an Organic Amendment for Enhancing Soil Quality and Sweet Basil (Ocimum basilicum L.) Growth: Agronomic and Ecotoxicological Evaluation. Agronomy 15. https://doi.org/10.3390/agronomy15051045\u003c/li\u003e\n \u003cli\u003eOueld Lhaj, M., Moussadek, R., Zouahri, A., Sanad, H., Saafadi, L., Mdarhri Alaoui, M., Mouhir, L., 2024b. Sustainable Agriculture Through Agricultural Waste Management: A Comprehensive Review of Composting\u0026rsquo;s Impact on Soil Health in Moroccan Agricultural Ecosystems. Agriculture 14. https://doi.org/10.3390/agriculture14122356\u003c/li\u003e\n \u003cli\u003eOueld Lhaj, Majda, Moussadek, R., Sanad, H., Manhou, K., Oueld Lhaj, M\u0026rsquo;hamed, Alaoui, M.M., Zouahri, A., Mouhir, L., 2025b. Ecological and Microbial Processes in Green Waste Co-composting for Pathogen Control and Evaluation of Compost Quality Index (CQI) Toward Agricultural Biosafety. https://doi.org/10.21203/rs.3.rs-7510199/v1\u003c/li\u003e\n \u003cli\u003ePanagos, P., Jones, A., Lugato, E., Ballabio, C., 2025. A Soil Monitoring Law for Europe. Global Challenges 9, 2400336.\u003c/li\u003e\n \u003cli\u003ePerović, M., Obradović, V., Zuber-Radenković, V., Knoeller, K., Mitrinović, D., Čepić, Z., 2024. The comprehensive evaluation of nitrate origin and transformation pathways in the oxic alluvial aquifer in Serbia. Environmental Science and Pollution Research 31, 33030\u0026ndash;33046. https://doi.org/10.1007/s11356-024-33403-w\u003c/li\u003e\n \u003cli\u003ePerumal, M., Sekar, S., Carvalho, P.C., 2024. Global investigations of seawater intrusion (SWI) in coastal groundwaters in the last two decades (2000\u0026ndash;2020): a bibliometric analysis. Sustainability 16, 1266.\u003c/li\u003e\n \u003cli\u003ePhilip, S., Nidhi, M., 2024. Performance Comparison of Artificial Neural Network and Random Forest Models for Predicting the Compressive Strength of Fibre-Reinforced GGBS-Based Geopolymer Concrete Composites. Materials Circular Economy 6, 34. https://doi.org/10.1007/s42824-024-00128-7\u003c/li\u003e\n \u003cli\u003ePoudel, B., Dahal, D., Banjara, M., Kalra, A., 2024. Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models. Forecasting 6, 1026\u0026ndash;1044. https://doi.org/10.3390/forecast6040051\u003c/li\u003e\n \u003cli\u003eRajeev, A., Shah, R., Shah, P., Shah, M., Nanavaty, R., 2025. The Potential of Big Data and Machine Learning for Ground Water Quality Assessment and Prediction. Archives of Computational Methods in Engineering 32, 927\u0026ndash;941. https://doi.org/10.1007/s11831-024-10156-w\u003c/li\u003e\n \u003cli\u003eRegasa, A., Haile, W., Abera, G., 2025. Effects of lime and vermicompost application on soil physicochemical properties and phosphorus availability in acidic soils. Scientific Reports 15, 25544. https://doi.org/10.1038/s41598-025-02053-4\u003c/li\u003e\n \u003cli\u003eRezaei, A., Hassani, H., 2018. Hydrogeochemistry study and groundwater quality assessment in the north of Isfahan, Iran. Environmental Geochemistry and Health 40, 583\u0026ndash;608. https://doi.org/10.1007/s10653-017-0003-x\u003c/li\u003e\n \u003cli\u003eRistea, E., Bisinicu, E., Lavric, V., Parvulescu, O.C., Lazar, L., 2025. A Long-Term Perspective of Seasonal Shifts in Nutrient Dynamics and Eutrophication in the Romanian Black Sea Coast. Sustainability 17. https://doi.org/10.3390/su17031090\u003c/li\u003e\n \u003cli\u003eRodier, J., 1985. L\u0026rsquo;analyse de l\u0026rsquo;eau, eaux naturelles, eaux r\u0026eacute;siduaires, eau de mer (7th ed.). Dunod, Paris.\u003c/li\u003e\n \u003cli\u003eSaleh, M.A., Rasel, H., 2024. Machine learning for groundwater levels: Uncovering the best predictors. Sustainable Water Resources Management 10, 166.\u003c/li\u003e\n \u003cli\u003eSanad, H., Mouhir, L., Zouahri, A., Moussadek, R., El Azhari, H., Yachou, H., Ghanimi, A., Oueld Lhaj, M., Dakak, H., 2024a. Assessment of Groundwater Quality Using the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Multivariate Statistical Analysis (MSA), and GIS Approaches: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 16, 1263. https://doi.org/10.3390/w16091263\u003c/li\u003e\n \u003cli\u003eSanad, H., Moussadek, R., Dakak, H., Zouahri, A., Oueld Lhaj, M., Mouhir, L., 2024b. Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 16, 2417. https://doi.org/10.3390/w16172417\u003c/li\u003e\n \u003cli\u003eSanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Dakak, H., Manhou, K., Zouahri, A., 2025a. Monte carlo simulation for evaluating spatial dynamics of toxic metals and potential health hazards in sebou basin surface water. Scientific Reports 15, 29471. https://doi.org/10.1038/s41598-025-15006-8\u003c/li\u003e\n \u003cli\u003eSanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Dakak, H., Zouahri, A., 2025b. Geospatial analysis of trace metal pollution and ecological risks in river sediments from agrochemical sources in Morocco\u0026rsquo;s Sebou basin. Scientific Reports 15, 16701. https://doi.org/10.1038/s41598-025-01199-5\u003c/li\u003e\n \u003cli\u003eSanad, H., Moussadek, R., Mouhir, L., Lhaj, M.O., Zahidi, K., Dakak, H., Manhou, K., Zouahri, A., 2025c. Ecological and Human Health Hazards Evaluation of Toxic Metal Contamination in Agricultural Lands Using Multi-Index and Geostatistical Techniques across the Mnasra Area of Morocco\u0026rsquo;s Gharb Plain Region. Journal of Hazardous Materials Advances 100724. https://doi.org/10.1016/j.hazadv.2025.100724\u003c/li\u003e\n \u003cli\u003eSanad, H., Moussadek, R., Mouhir, L., Oueld Lhaj, M., Dakak, H., El Azhari, H., Yachou, H., Ghanimi, A., Zouahri, A., 2024c. Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Agronomy 14. https://doi.org/10.3390/agronomy14061112\u003c/li\u003e\n \u003cli\u003eSanad, H., Oueld lhaj, M., Zouahri, A., Saafadi, L., Dakak, H., Mouhir, L., 2024d. Groundwater pollution by nitrate and salinization in Morocco: a comprehensive review. Journal of Water and Health 22, 1756\u0026ndash;1773. https://doi.org/10.2166/wh.2024.024\u003c/li\u003e\n \u003cli\u003eScanlon, B.R., Fakhreddine, S., Rateb, A., de Graaf, I., Famiglietti, J., Gleeson, T., Grafton, R.Q., Jobbagy, E., Kebede, S., Kolusu, S.R., Konikow, L.F., Long, D., Mekonnen, M., Schmied, H.M., Mukherjee, A., MacDonald, A., Reedy, R.C., Shamsudduha, M., Simmons, C.T., Sun, A., Taylor, R.G., Villholth, K.G., V\u0026ouml;r\u0026ouml;smarty, C.J., Zheng, C., 2023. Global water resources and the role of groundwater in a resilient water future. Nature Reviews Earth \u0026amp; Environment 4, 87\u0026ndash;101. https://doi.org/10.1038/s43017-022-00378-6\u003c/li\u003e\n \u003cli\u003eSerati, P., Sadatinejad, S.J., Yousefi, H., Mirzavand, M., 2025. Delineating the effect of water/rock\u0026ndash;water/clay interactions and ion exchange in groundwater and soil salinization in an arid and semi-arid region of Iran. Environmental Earth Sciences 84, 405. https://doi.org/10.1007/s12665-025-12402-1\u003c/li\u003e\n \u003cli\u003eSeyar, M.H., Ahamed, T., 2024. Optimization of Soil-Based Irrigation Scheduling Through the Integration of Machine Learning, Remote Sensing, and Soil Moisture Sensor Technology, in: Ahamed, T. (Ed.), IoT and AI in Agriculture: Smart Automation Systems for Increasing Agricultural Productivity to Achieve SDGs and Society 5.0. Springer Nature Singapore, Singapore, pp. 275\u0026ndash;299. https://doi.org/10.1007/978-981-97-1263-2_18\u003c/li\u003e\n \u003cli\u003eShah, S.S., van Dam, J., Singh, A., Kumar, Suresh, Kumar, Satyendra, Bundela, D.S., Ritsema, C., 2025. Impact of irrigation, fertilizer, and pesticide management practices on groundwater and soil health in the rice\u0026ndash;wheat cropping system\u0026mdash;a comparison of conventional, resource conservation technologies and conservation agriculture. Environmental Science and Pollution Research 32, 533\u0026ndash;558.\u003c/li\u003e\n \u003cli\u003eShqerat, N., Al-Tabbal, J., 2025. Potential reuse of greywater for irrigation of tomato (Solanum lycopersicum) plants and its effect on plants growth and soil. International Journal of Phytoremediation 27, 561\u0026ndash;582. https://doi.org/10.1080/15226514.2024.2427926\u003c/li\u003e\n \u003cli\u003eShu, P., Aslam, R.W., Naz, I., Ghaffar, B., Kucher, D.E., Quddoos, A., Raza, D., Abdullah-Al-Wadud, M., Zulqarnain, R.M., 2025. Deep learning-based super-resolution of remote sensing images for enhanced groundwater quality assessment and environmental monitoring in Urban Areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.\u003c/li\u003e\n \u003cli\u003eSilwani, T., Themba, N., Chokwe, T.B., Semenya, K., 2025. Assessment of Groundwater Quality, Heavy Metal Contamination, and Human Health Risks in Roundhill Municipal Landfill, Eastern Cape, South Africa. Journal of Environmental Health and Sustainable Development 10, 2666\u0026ndash;2693.\u003c/li\u003e\n \u003cli\u003eSomay-Altas, M., Sanli, E., 2025. Urban Wetlands as EcoHaven Oasis: Hydrogeochemical Insights from the Inciralti-Cakalburnu Urban Wetland (ICUW) in Izmir, Turkiye. Water, Air, \u0026amp; Soil Pollution 236, 208. https://doi.org/10.1007/s11270-025-07840-3\u003c/li\u003e\n \u003cli\u003eSuh, G.C., Afahnwie, N.A., Tiabou, A.F., Djibril, K.N.G., Meniemoh, A.R., Yiika, L.P., 2025. Source apportionment, ecological and toxicological risk assessment of trace metals in agricultural soils of Wabane, South West Region, Cameroon. Journal of Trace Elements and Minerals 12, 100218. https://doi.org/10.1016/j.jtemin.2025.100218\u003c/li\u003e\n \u003cli\u003eTackley, H.A., Kurylyk, B.L., Lake, C.B., 2025. Saltwater Intrusion Vulnerability of Soil and Groundwater Near Estuaries. Wiley Interdisciplinary Reviews: Water 12, e70031.\u003c/li\u003e\n \u003cli\u003eTefera, M.L., Carletti, A., Altea, L., Rizzu, M., Migheli, Q., Seddaiu, G., 2024. Land degradation and the upper hand of sustainable agricultural intensification in sub-Saharan Africa-A systematic review. Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS) 125, 63\u0026ndash;83.\u003c/li\u003e\n \u003cli\u003eThakur, T., Rishi, M.S., Naik, P.K., Sharma, P., 2016. Elucidating hydrochemical properties of groundwater for drinking and agriculture in parts of Punjab, India. Environmental Earth Sciences 75, 467. https://doi.org/10.1007/s12665-016-5306-1\u003c/li\u003e\n \u003cli\u003eTiabou, A.F., Atabe, G.A.A., Sigue, C., Yiika, L.P., Kachoueiyan, F., Forchenallah, N.E., 2024a. Appraisal of pollution, ecological and health risks assessment of trace metals in soils of Logbadjeck quarrying area, Nyong Series, Cameroon. Journal of Trace Elements and Minerals 10, 100204. https://doi.org/10.1016/j.jtemin.2024.100204\u003c/li\u003e\n \u003cli\u003eTiabou, A.F., Esuka, C.N., Mbowou Ngantche, I.F., Yiika, L.P., Wadgua, B.P., Agyingi, C.M., 2024b. Spatial Distribution, Contamination Characteristics and Eco-Risk Assessment of Toxic Metals in Stream Sediments of Njombe-Penja Banana Plain, Cameroon Volcanic Line. Chemistry Africa 1\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eTiabou, A.F., Manka\u0026rsquo;a, F.C., Yiika, L.P., Mengu Junior, E.E., 2025. Potentially toxic metal contamination, pollution, and ecological risk assessment in volcanic soils of Njombe-Penja Banana Plain, Cameroon Volcanic Line. Arabian Journal of Geosciences 18, 180. https://doi.org/10.1007/s12517-025-12317-y\u003c/li\u003e\n \u003cli\u003eWang, L., Ju, C., Han, C., Yu, Z., Bai, M., Wang, C., 2025. The interaction of nutrient uptake with biotic and abiotic stresses in plants. Journal of Integrative Plant Biology 67, 455\u0026ndash;487.\u003c/li\u003e\n \u003cli\u003eWedding, L.M., Pittman, S., Lepczyk, C., Parrain, C., Puniwai, N., Boyle, J., Goldberg, E., Young, M., Marty, P., Wilhelm, K., 2024. Integrating the multiple perspectives of people and nature in place-based marine spatial planning. npj Ocean Sustainability 3, 43.\u003c/li\u003e\n \u003cli\u003eWen, S., Wen, M., Liang, S., Pang, G., Fan, J., Dong, M., Wang, Y., Zhang, J., Ye, Y., 2024. Spatial Distribution and Mechanisms of Groundwater Hardness in the Plain Area of Tangshan City, China. Water 16. https://doi.org/10.3390/w16243627\u003c/li\u003e\n \u003cli\u003eWHO, 2017. World Health Organization. Guidelines for Drinking-Water Quality: Fourth Edition Incorporating First Addendum. World Health Organization: Geneva, Switzerland.\u003c/li\u003e\n \u003cli\u003eWurl, J., Imaz-Lamadrid, M.A., Mendez-Rodriguez, L.C., Hern\u0026aacute;ndez-Morales, P., 2023. Hydrochemical Indicator Analysis of Seawater Intrusion into Coastal Aquifers of Semiarid Areas. Resources 12. https://doi.org/10.3390/resources12040047\u003c/li\u003e\n \u003cli\u003eXu, Q., Yang, F., Hu, S., He, X., Hong, Y., 2024. Tree Height\u0026ndash;Diameter Model of Natural Coniferous and Broad-Leaved Mixed Forests Based on Random Forest Method and Nonlinear Mixed-Effects Method in Jilin Province, China. Forests 15. https://doi.org/10.3390/f15111922\u003c/li\u003e\n \u003cli\u003eYassin, M.A., Abba, S.I., Shah, S.M.H., Usman, A.G., Egbueri, J.C., Agbasi, J.C., Khogali, A., Baalousha, H.M., Aljundi, I.H., Sammen, S.Sha., Scholz, M., 2024. Toward Decontamination in Coastal Regions: Groundwater Quality, Fluoride, Nitrate, and Human Health Risk Assessments within Multi-Aquifer Al-Hassa, Saudi Arabia. Water 16. https://doi.org/10.3390/w16101401\u003c/li\u003e\n \u003cli\u003eZafar, S., Khan, M.K., Aslam, N., Hasnain, Z., 2024. Impact of Different Stresses on Morphology, Physiology, and Biochemistry of Plants, in: Shahid, M., Gaur, R. (Eds.), Molecular Dynamics of Plant Stress and Its Management. Springer Nature Singapore, Singapore, pp. 67\u0026ndash;91. https://doi.org/10.1007/978-981-97-1699-9_4\u003c/li\u003e\n \u003cli\u003eZahi, F., Drouiche, A., Medjani, F., Azzeddine, R., Djidel, M., 2024. Hydrogeochemical processes controlling surface water quality for irrigation in a Mediterranean wetland ecosystem, Northeast Algeria. Environmental Monitoring and Assessment 196, 881. https://doi.org/10.1007/s10661-024-13019-w\u003c/li\u003e\n \u003cli\u003eZeng, L., Yang, F., Chen, Y., Chen, S., Xu, M., Gu, C., 2024. Temperature and Dissolved Oxygen Drive Arsenic Mobility at the Sediment\u0026mdash;Water Interface in the Lake Taihu. Toxics 12. https://doi.org/10.3390/toxics12070471\u003c/li\u003e\n \u003cli\u003eZhang, Haitao, Jiang, B., Zhang, Haiqin, Li, P., Wu, M., Hao, J., Hu, Y., 2024. Water\u0026ndash;Rock Interaction Mechanisms and Hydrochemical Evolution in the Underground Reservoirs of Coal Mines. ACS Omega 9, 43834\u0026ndash;43849. https://doi.org/10.1021/acsomega.4c06809\u003c/li\u003e\n \u003cli\u003eZhang, Q., You, X., 2024. Recent advances in surface water quality prediction using artificial intelligence models. Water Resources Management 38, 235\u0026ndash;250.\u003c/li\u003e\n \u003cli\u003eZhang, Y., Li, H., Zhang, L., Xue, Y., 2025. Seawater intrusion impacts on groundwater and soil quality in the alluvial fan of the Xiaoling River, Liaoning, China. Human and Ecological Risk Assessment: An International Journal 1\u0026ndash;23.\u003c/li\u003e\n \u003cli\u003eZouahri, A., Dakak, H., Douaik, A., El Khadir, M., Moussadek, R., 2015. Evaluation of groundwater suitability for irrigation in the Skhirat region, Northwest of Morocco. Environmental Monitoring and Assessment 4184.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater Quality, Seawater Intrusion (SWI), Random Forest (RF), Artificial Neural Networks (ANN), Hydrogeochemical Analysis, Morocco","lastPublishedDoi":"10.21203/rs.3.rs-7705609/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7705609/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the quality and spatial variability of groundwater in the coastal agricultural zone of Skhirat, Morocco, under growing environmental and anthropogenic stress. The main objectives were to assess hydrogeochemical characteristics, evaluate groundwater suitability for drinking and irrigation, quantify saltwater intrusion, and model quality indices using artificial intelligence. Groundwater (GW) samples were collected and analyzed for physico-chemical parameters. Hydrogeochemical characterization was performed using Piper, Gibbs, and Chadha diagrams. Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), and Saltwater Mixing Index (SMI) were computed using standard equations. Statistical analyses included correlation matrices, Principal Component Analysis (PCA), and K-means clustering. Machine learning models (Random Forest (RF) and Artificial Neural Networks (ANN)) were applied to predict WQI, IWQI, and SMI, followed by spatial interpolation using GIS approach. Results revealed that WQI values ranged from 31.58 to 139.28, with 40% of samples falling in the \"poor\" to \"very poor\" categories. IWQI indicate that 43.3% of samples were classified as \"good\" and 6.7% as \"very poor\" for irrigation practices. SMI values \u0026gt;1, indicating seawater intrusion, were observed in 30% of samples. The ANN model achieved high predictive accuracy for IWQI (R²=0.81), while RF performed best for SMI (R²=0.74). Spatial analysis confirmed salinization patterns toward coastal zones. These findings highlight the value of integrated AI and geostatistical approaches for sustainable groundwater monitoring and management in vulnerable coastal aquifers.\u003c/p\u003e","manuscriptTitle":"AI and Machine Learning-Based Spatial Modeling of Groundwater Quality Indices and Hydrogeochemistry for Accurate Prediction of Seawater Intrusion and Irrigation Sustainability in Coastal Agroecosystems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 20:36:24","doi":"10.21203/rs.3.rs-7705609/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67a24f46-6077-4ec0-85c2-c11ae6bb5124","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55284101,"name":"Environmental Engineering"},{"id":55284102,"name":"Agrochemicals"}],"tags":[],"updatedAt":"2025-09-26T20:36:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 20:36:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7705609","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7705609","identity":"rs-7705609","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}