Water is a fundamental natural resource essential for sustaining life, promoting human health, and driving economic development. It plays a critical role in food security, poverty reduction, and maintaining ecological balance [1] . However, the quality of water resources worldwide has been deteriorating due to both natural processes and human activities [2] . Groundwater, a major source of water for domestic, agricultural, and industrial purposes, faces significant threats from contamination, over-exploitation, and pollution [3] .

In India, groundwater quality has declined due to several factors, including over-extraction without adequate recharge, excessive use of chemicals in agriculture, and the percolation of pollutants such as pesticides and fertilizers into subsurface water tables [4] . Industrial and domestic effluents, often untreated, further exacerbate the problem, leading to contamination and the loss of safe drinking water [5] . This presents a critical challenge, as access to clean and safe drinking water is a fundamental human right and essential for public health and development [6] .

Particularly, Visakhapatnam District in Andhra Pradesh faces growing concerns over the suitability of its groundwater for domestic and irrigation purposes. The region has witnessed the effects of both natural geological factors and anthropogenic influences on groundwater quality [7] . The hydrochemical properties of water, including pH, electrical conductivity, and the concentration of major ions such as Ca²⁺, Mg²⁺, Na⁺, and ${\text{HCO}}_3^{-}$, are crucial in determining its usability for domestic consumption and agricultural irrigation [8] . Excessive levels of dissolved salts can lead to salinity issues, which reduce soil permeability and adversely affect crop yields [9] .

To address these challenges, advanced technologies such as remote sensing, Geographic Information Systems (GIS), and machine learning are increasingly being utilized to assess groundwater suitability [10] . These technologies enable the spatial mapping of groundwater quality and help in the development of data-driven models for decision-making in water resource management [11] . This study leverages an AI-driven Spatial Decision Support System (SDSS) to revolutionize groundwater quality analysis in Visakhapatnam District. By integrating remote sensing, GIS and machine learning, the study aims to provide a comprehensive evaluation of groundwater suitability for both domestic and irrigation purposes, ensuring sustainable water resource management for the region’s future [12] [13] . While previous studies have successfully utilized AI/ML for water quality classification [14] and predictive modeling [15] , this study pioneers the integration of AI-driven Spatial Decision Support Systems (SDS) to simultaneously analyze multi-parameter datasets, predict future scenarios and map spatial variations. This holistic approach not only enhances accuracy but also provides actionable insights for targeted remediation efforts in resource-limited settings.

Visakhapatnam District ( Figure 1 ), located in the southeastern part of India, serves as the focus of this study due to its significant reliance on groundwater for domestic, irrigation, and industrial purposes. The district encompasses an area of 11,343 km² and experiences an average annual rainfall of 1202 mm, which plays a crucial role in recharging groundwater resources. The diverse geomorphology of the region includes structural hills, pediplains and alluvial plains, all of which contribute to its hydrological dynamics. Major rivers such as the Machkund, Tandava, Varaha, Sarada, and Gosthani further influence the groundwater system, although large-scale groundwater extraction remains the primary method for meeting water demands.

The total irrigated area in the district is approximately 113,246 ha, with surface water accounting for 58,521 ha, groundwater for 29,369 ha, and other sources for 25,356 ha. This highlights the significant dependence on groundwater for agricultural activities. The predominant soil types are red sandy loam and clayey soils, which affect water retention and suitability for various crops. Additionally, the geology of the area consists of formations ranging from ancient Archean to more recent sediments, including khondalite and alluvium, influencing groundwater quality and availability.

The groundwater quality data for this study was sourced from 63 exploratory wells located throughout the Visakhapatnam District. This data was obtained from the Central Ground Water Board (CGWB), Visakhapatnam. The collection mechanism involved systematic sampling procedures to ensure the accuracy and reliability of the water quality parameters, which include pH, electrical conductivity, total dissolved solids, and major ion concentrations ( Table 1 ).

ArcGIS v.10.8 software was utilized to analyze and visualize data pertaining to groundwater quality. The software was used to perform spatial analysis to generate detailed spatial distribution map output that facilitates a comprehensive understanding of groundwater suitability across the study area.

The integration of AI-driven Spatial Decision Support Systems (SDS) was pivotal in enhancing groundwater quality assessment in the Visakhapatnam District. This approach combined advanced data analytics, predictive modeling, and visualization tools to provide actionable insights for water resource management.

Initially, comprehensive datasets were gathered from multiple monitoring wells and remote sensing data. Key parameters analyzed included alkalinity, chloride, electrical conductivity (EC), nitrate concentrations, and total dissolved solids (TDS), as highlighted by [16] [17] . Data preprocessing involved cleaning and normalizing these datasets to ensure accuracy. Missing values were addressed using interpolation methods, and outliers were identified through statistical techniques, following best practices in data management [18] . The methodologies for calculation of various indices for agricultural suitability are shown in Table 2 below.

All the values are expressed in meq/L.

Various water quality indices such as Sodium Adsorption Ratio (SAR), Percentage Sodium (% Na), Residual Sodium Carbonate (RSC), Kelly’s Ratio (KR), and Magnesium Adsorption Ratio (MAR) were integrated into AI/ML models to assess groundwater quality and its suitability for different uses. These indices, critical in determining the salinity and alkalinity of water, were handled using advanced machine learning algorithms that enabled the extraction of insights from complex, multidimensional datasets.

This study utilized an ensemble of clustering and regression models within the SDS framework to analyze interdependencies between nitrate, chloride and uranium levels, achieving a predictive accuracy 15% higher than existing approaches. Furthermore, the system generated real-time spatial heatmaps, enabling hyper-localized interventions, which is a feature not typically addressed in current AI/ML applications.

The AI models utilized in this study includes, Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were chosen for their ability to model non-linear and complex relationships between groundwater quality parameters and environmental conditions. Each of these models was tailored to process the indices and derive meaningful conclusions regarding groundwater quality and suitability. The dataset, comprising 50 groundwater quality samples with parameters such as pH, TDS, EC, nitrates, and chloride, was split into training (80%) and testing (20%) subsets. Data preprocessing included handling missing values through mean imputation and normalizing features to a 0 - 1 scale. Feature selection was performed based on correlation analysis, retaining parameters with >0.7 correlation with groundwater quality indices.

Random Forest (RF), an ensemble learning method, was used to build multiple decision trees to enhance predictive accuracy. It evaluated the relative importance of different indices such as SAR, % Na, and RSC in determining overall water quality. By using feature importance scores from RF, the most influential indices for groundwater suitability were identified, allowing the model to focus on the key factors affecting water quality [19] . RF’s strength lies in its ability to handle large and diverse datasets, making it effective in exploring the relationships between indices and water quality.

Support Vector Machine (SVM) was employed for classification tasks, where groundwater samples were categorized into suitable and unsuitable classes based on the indices. The model’s ability to find an optimal hyperplane that maximizes the separation between different classes ensured high accuracy in distinguishing between water types [20] . By incorporating indices like SAR and KR, SVM effectively identified groundwater that posed risks for agricultural use due to elevated sodium or carbonate levels.

Artificial Neural Networks (ANN), specifically multilayer perceptrons, were used to model the complex, non-linear relationships between the indices and environmental conditions. The architecture of ANNs allowed the model to learn intricate patterns from historical data, making it particularly useful for predicting future groundwater conditions [21] . ANNs were trained to understand how fluctuations in indices like MAR and RSC could signal changes in water quality over time, providing valuable insights for sustainable water resource management. Training the AI models involved a comprehensive dataset that included groundwater quality parameters (e.g., SAR, % Na, RSC) along with environmental conditions such as temperature, rainfall, and land use patterns. To ensure model efficiency, feature selection techniques such as Recursive Feature Elimination (RFE) and feature importance scoring from the RF model were employed. These techniques helped to filter out less relevant parameters, focusing on key indices that significantly impacted water quality predictions. The dataset was divided into training and testing sets, and cross-validation methods were used to avoid overfitting. Hyperparameter tuning was conducted using grid search to optimize model parameters for better accuracy and precision (Mishra et al., 2019). The integration of AI/ML methods allowed for advanced analysis of water quality indices like SAR, % Na, RSC, KR, and MAR. By leveraging the strengths of RF, SVM, and ANNs, the study provided valuable insights into groundwater suitability, enabling more accurate predictions and better decision-making for sustainable water resource management. The use of these indices in AI models helped unravel the complexities of groundwater chemistry and environmental interactions, offering a robust framework for future studies in water quality assessment.

Spatial distribution maps were generated in the GIS environment based on the predictions from the AI models. This process involved geostatistical analysis, employing methods such as kriging and inverse distance weighting to interpolate groundwater quality parameters across the district [22] . The output from the AI models was integrated into GIS for visual representation, enabling the display of variations in groundwater quality and the identification of hotspots and areas requiring further investigation.

The AI-driven SDS framework facilitated the integration of real-time data from monitoring wells, allowing dynamic updates to the predictive models. This capability supports adaptive management, enabling stakeholders to make timely decisions based on current groundwater conditions. Simulating different management scenarios further evaluated potential impacts on groundwater quality, providing a comprehensive view of resource sustainability.

The spatial analyses conducted reveal significant variations in water quality parameters across the study area. Many locations exceed permissible limits for critical constituents, posing risks to both human health and agricultural productivity. Immediate intervention is necessary to manage water quality effectively. Spatial analyses of various water quality parameters—Alkalinity, Chloride, Electrical Conductivity (EC), Nitrate, Total Dissolved Solids (TDS), Total Hardness (TH), Fluoride, pH and Uranium were conducted using GIS. The results of these analyses are detailed below.

The assessment of groundwater quality in the Visakhapatnam District reveals critical challenges regarding its suitability for domestic and agricultural use. By integrating AI-driven Spatial Decision Support Systems (SDS), this study effectively identifies existing water quality issues while enhancing predictive modeling of groundwater parameters. This integration ultimately supports the development of targeted management strategies for this vital resource.

This study evaluated groundwater quality in the Visakhapatnam District for domestic and irrigation use, analyzing fifty samples against WHO guidelines and irrigation standards. Most samples met domestic suitability criteria, though Kothakota exhibited anomalies, indicating unsuitable groundwater. Parameters like SAR, KR, Na%, RSC, and MAR were assessed for irrigation. The majority of samples (96%) fell into the “good” and “suitable” categories (C1-S1, C2-S1, C3-S1), indicating low salinity and sodium levels. However, 4% were classified as C4-S1 and C4-S4, unsuitable for irrigation. The integration of AI-driven Spatial Decision Support Systems (SDS) enhanced groundwater quality assessment through robust data analysis and predictive modeling. Real-time monitoring systems leveraging AI will be crucial for maintaining groundwater quality and ensuring the sustainability of these vital resources, promoting public health and sustainable agricultural practices in the Visakhapatnam District. The analysis reveals that many groundwater sources in the study area are unsuitable for domestic consumption due to elevated levels of key parameters. Urgent measures are required to address these quality issues, ensuring safe drinking water for the local population. Continuous monitoring and management strategies should be implemented to improve groundwater quality and protect public health. This study sets itself apart by combining AI-driven SDS with spatial analytics to address both immediate and long-term challenges in groundwater quality management. By offering a framework that enhances predictive accuracy and operational applicability, it establishes a scalable solution for sustainable resource management, particularly in data-scarce regions.