Groundwater Vulnerability Assessment Research Articles

Enhancing groundwater vulnerability assessment for improved environmental management: addressing a critical environmental concern

Groundwater serves as a primary water source for various purposes. Therefore, aquifer pollution poses a critical threat to human health and the environment. Identifying the aquifer’s highly vulnerable areas to pollution is necessary to implement appropriate remedial measures, thus ensuring groundwater sustainability. This paper aims to enhance groundwater vulnerability assessment (GWVA) to manage aquifer quality effectively. The study focuses on the El Orjane Aquifer in the Moulouya basin, Morocco, which is facing significant degradation due to olive mill wastewater. Groundwater vulnerability maps (GVMs) were generated using the DRASTIC, Pesticide DRASTIC, SINTACS, and SI methods. To assess the effectiveness of the proposed improvements, 24 piezometers were installed to measure nitrate concentrations, a common indicator of groundwater contamination. This study aimed to enhance GWVA by incorporating new layers, such as land use, and adjusting parameter rates based on a comprehensive sensitivity analysis. The results demonstrate a significant increase in Pearson correlation values (PCV) between the produced GVMs and measured nitrate concentrations. For instance, the PCV for the DRASTIC method improved from 0.42 to 0.75 after adding the land use layer and adjusting parameter rates using the Wilcoxon method. These findings offer valuable insights for accurately assessing groundwater vulnerability in areas with similar hazards and hydrological conditions, particularly in semi-arid and arid regions. They contribute to improving groundwater and environmental management practices, ensuring the long-term sustainability of aquifers.

Assessing Groundwater Resources Vulnerability with the New DRASTIC-LP: A Case Study in Chiang Mai Province, Thailand

Groundwater vulnerability has gained widespread attention, particularly in Chiang Mai Province, one of Thailand’s fastest-growing cities, which is experiencing rapid development in both economic and social sectors. The expansion of urban communities and the industrial, tourism, and agriculture sectors has resulted in the overutilization of available resources, notably water resources. This overuse, coupled with the adoption of modern technology to boost productivity and meet market demands, has led to an increased reliance on groundwater to supplement surface water sources, providing benefits across all sectors. However, the economic and social growth plays a pivotal role in shaping the diversity of land use, encompassing residential, commercial, industrial, and agricultural activities. These activities, in turn, directly contribute to environmental pollution, particularly in terms of the risk of groundwater contamination in Chiang Mai Province. This study aims to predict the future vulnerabilities of groundwater resources under an ensemble of climate change scenarios and changes in land-use patterns. Chiang Mai Province in northern Thailand is one of the fastest-growing cities and therefore is experiencing rapid urbanization, as well as land-use pattern changes, which was important for the case study. The new DRASTIC model, namely the DRASTIC-LP model, combined with GIS-based techniques and overlay techniques, was used to generate the map of groundwater resource vulnerabilities. A point pollution source (P)-related land-use pattern (L) that represents contamination impacts was considered an additional new DRASTIC parameter. The study’s findings reveal the high reliability and maximum effectiveness of the new DRASTIC-LP model in assessing groundwater vulnerability and contamination-risk areas under a climate change scenario (by MIROC-ESM-CHEM model under RCP.8.5 scenario) and land-use pattern changes (by CA_Markov Chian Model) for both the current year (2020) and the next 50-year period (2021–2070). Furthermore, the new DRASTIC-LP model is employed to trace the movement of pollutants from high- to very high-risk areas based on the groundwater vulnerability and contamination-risk maps. The results highlight that waste disposal dumping sites pose a more critical distribution and movement of pollutants when compared to industrial sites. Additionally, unconsolidated aquifers and cracked consolidated rock aquifers show a potentially higher occurrence of pollutant distribution and movement when compared to consolidated aquifers. Consequently, the study’s outcomes are applied to formulate guidelines for the management and control of groundwater resource contamination. These guidelines serve as valuable tools for decision makers, aiding in pollution prevention and the effective management of contamination risks in groundwater resources.

Introducing reverse Multi Influencing Factor technique in DRASTIC model for groundwater vulnerability assessment

Effective groundwater resource management has become essential to the development of urbanized areas, particularly in regions with significant agricultural and industrial activity. In the present study, a multi-criteria decision-analysis techniques methodology was used to assess the groundwater vulnerability of Seonath basin. The objective of this study is to identify the areas that are more susceptible to groundwater contamination by incorporating various hydrogeological layers from DRASTIC into the geographical information system (GIS) platform. To enhance the weights of the DRASTIC parameters used for the identification of the groundwater vulnerability index values, apart from the application of the widely-used multiple influencing factors (MIF) method, a new modified MIF method (hereafter named RMIF) was also introduced and applied. Based on their index map, the final results were classified into five vulnerable classes: very low, low, moderate, high, and very high. Lower index values indicated areas that were less vulnerable, and vice versa. DRASTIC-RMIF is performing better than DRASTIC and DRASTIC-MIF, respectively majority of the total area lies in moderately vulnerable zones: 77.50 %, 70.52 % and 28.20 %. In order to validate the groundwater vulnerability zones, we also attempted to test and create a relationship between nitrate and the final groundwater vulnerability result. And the coefficient of determination of DRASTIC, DRASTIC–MIF, and DRASTIC–RMIF methods were 0.6, 0.4, and 0.8, respectively. From this validation DRASTIC-RMIF obtained the most accurate result among all weighting techniques due to local hydrogeological condition. Model validation showed a significant association between the sample nitrate concentration values and the DRASTIC-RMIF model index values. Decision/Policy makers must identify vulnerable zones in order to manage and maintain an identification system for groundwater supplies.

Application of geochemical tracers for the investigations of groundwater resources in semi‐arid region (Central‐East Tunisia)

AbstractGroundwater resources in Mahdia‐Ksour Essef region, located in the central‐eastern part of Tunisia, suffer from intensive exploitation and degradation of water quality. The assessment of groundwater vulnerability, the well‐understanding of geochemical processes and the investigation of groundwater quality variations are of particular importance for water resources management in this semi‐arid region. The results of this multi‐disciplinary investigation show that groundwaters of Mahdia‐Ksour Essef region are flowing from El Jem and Boumerdes regions in the South, towards the Mediterranean Sea and the sebkha of Moknine in the North. The groundwaters are classified as either Na‐Cl or Ca‐SO4. Stable oxygen and hydrogen isotope compositions (δ18O and δ2H) confirm a recharge by rapid water infiltration, evaporate surface water and water‐rock interaction processes in the salinity increase. The rock‐water interaction processes of the mineralization of groundwaters include the cation‐exchange reactions and the dissolution of carbonates and evaporate. The high values of the water quality index (WQI) and of the total dissolved solids (TDS) (3.7–11 g/L) of these groundwaters indicate their unsuitability for drinking purpose. Moreover, the combination of the WQI, TDS and nitrate results prove the vulnerability of the studied groundwater to the anthropogenic pollution linked to agriculture and domestic activities and to the salt water contamination.

GIS-based assessment of groundwater vulnerability to heavy metal contamination via water quality pollution indices in urban Aligarh, India

Abstract The study presents an assessment of groundwater vulnerability due to heavy-metal contamination using Heavy Metal Pollution and Contamination Index of Urban Aligarh. Globally, hazardous compounds in industrially contaminated sites are pressing and high-priority issue. A detailed risk assessment was carried out to determine the potential health hazards linked to locations that were recently polluted. A total of 17 groundwater samples were taken from hand-pump and 20 industrial drainage samples were collected from selected areas of Aligarh. The concentration of heavy-metals in the collected samples analyzed were shown on maps using ArcGIS software and interpreted for Heavy Metal Pollution Index (HPIx) and Contamination Index (CDx). These analyzed values were subsequently compared with the permissible limits established by the agencies like EPA, WHO, and BIS. The mean concentration of heavy-metals in groundwater of different locations was observed as follows particular sequence: Ni (1.40), Cu (0.58), Zn (0.06), Fe (0.08), Mn (0.04), Cr (0.001), Pb (0.00025) mg/l. Additionally in industrial effluent, Cr (18.3), Ni (13.34), Mn (1.16), Cu (1.99), Pb (1.2), Fe (6.3), Zn (0.51) mg/l. According to HPIx, the analysis reveals 64.7%, of visited areas belonged to have safe groundwater. Conversely, a smaller proportion, 35.3%, was found falling into heavy metal-polluted group.

A Critical Review of Climate Change Impacts on Groundwater Resources: A Focus on the Current Status, Future Possibilities, and Role of Simulation Models

The Earth’s water resources, totalling 1.386 billion cubic kilometres, predominantly consist of saltwater in oceans. Groundwater plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as a drinking water source and 60–70% for irrigation. Climate change, with temperature increases and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies employ numerical models to assess climate change impacts on groundwater recharge that could help in the management of groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. This review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography and groundwater contamination processes, gradual sea level rise scenarios, and realistic representations of the region of study. With the advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence could assist in the efforts to improve models for assessing the impacts of climate change on hydrological modelling.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Assessing groundwater vulnerability and addressing salinization in the coastal region of Ba Ria–Vung Tau province, Vietnam: an enhanced DRASTIC model approach

Assessing groundwater vulnerability and addressing salinization in the coastal region of Ba Ria–Vung Tau province, Vietnam: an enhanced DRASTIC model approach

Groundwater Vulnerability Assessment of Shalamzar Aquifer using GIS-Based DRASTIC Model

Groundwater Vulnerability Assessment of Shalamzar Aquifer using GIS-Based DRASTIC Model

GIS based Groundwater Vulnerability Assessment of Lahore a Metropolitan using Modified DRASTIC Model

The metropolitans of the developing countries are experiencing the hazard of low quality drinking water. Lahore, the heart of Pakistan, has over 12 million population and is going to face the shortage of clean drinking water. The study was designed to create a groundwater vulnerability map of Lahore using modified DRASTIC model. The water table, land use/cover, hydraulic conductivity, vadose zone, soil and aquifer media layers of Lahore aquifer were analyzed using ‘geostatistical analyst’ and ‘spatial analyst’ extensions of ArcGIS software to compute the groundwater vulnerability. The Vulnerability index of modified DRASTIC model ranged from 64 – 144. The spatial variability of modified DRASTIC index were divided into 4 categories, namely, Low, Moderate, High and Very High groundwater vulnerable zones. The modified DRASTIC model revealed that 51.38% areas have low vulnerability, 36.77% areas have moderate vulnerability, 9.48% areas have high vulnerability and 2.37% areas have very high vulnerability to groundwater pollution. The ‘high’ and ‘very high’ vulnerability classes were found in the western parts of Lahore. The results of modified DRASTIC model were validated using the groundwater contamination data of arsenic in the study area. The groundwater arsenic concentrations map also showed higher values in the northwestern parts while in the eastern parts of the study area, the arsenic concentrations had a decreasing trend. In Ravi Town, the arsenic concentrations were positively correlated with modified DRASTIC vulnerability index and inversely correlated with water table depth (p < 0.01). It is recommended that the arsenic removal plants should be installed at all the tubewells with higher arsenic levels (arsenic concentration > 50 ppb) to provide safe drinking water to the citizens of Lahore. It is recommended that the modified DRASTIC model based on geospatial techniques can be applied on other metropolitans of the world for taking preventive measures against massive water pollution.

Modelling groundwater vulnerability in a vulnerable deltaic coastal region of Sundarban Biosphere Reserve, India.

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems.

A comparison of methods for assessing groundwater vulnerability in karst aquifers: the case study of Terminio Mt. aquifer (Southern Italy)

The assessment of groundwater vulnerability to pollution is becoming even more important all over the world due to the increase of impacts of human activities on groundwater resources and the related risks to the human health, economics, and the environment. Owing to the variability of methods known for estimating groundwater vulnerability, basically depending on hydrogeological parameters considered and the scale of analysis, the comparison of results of different methods appears straightforward for identifying the best approach in a given hydrogeological condition and reference scale. In such a view, this work attempts to assess the groundwater vulnerability of the Terminio Mt. karst aquifer, by applying four different groundwater vulnerability methods, index-based, and comparing results in order to identify the best performing one in karst environments. The study aquifer, located in the Picentini Mts Regional Park (Campania region, southern Italy) represents a strategic drinking water resource since Roman times and hosts massive groundwater resources which outflow mainly from tapped basal and subordinately perched springs.The peculiar characters of the study karst aquifer, which favour direct infiltration and groundwater recharge processes, as well as the occurrence of industrial, agricultural and grazing activities, make it very vulnerable to groundwater pollution, thus requiring a proper and careful territorial management.Beside the most frequently and generally used methods for assessing groundwater vulnerability, such as the DRASTIC and SINTACS, also DAC and COP methods specifically designed for karst aquifers were applied and mutually compared. Results of SINTACS, DRASTIC and DAC methods show groundwater vulnerability maps of the Terminio Mt. karst aquifer as chiefly characterized by two classes of intrinsic groundwater vulnerability, varying between the medium and high degrees. Furthermore, high and extremely high values of groundwater vulnerability were found in areas controlled by the shallow depth of the water-table. Instead, the COP method resulted as the most effective in identifying the endorheic areas and the related karst morphologies as very high groundwater vulnerability zones, therefore the most suitable in capturing specific hydrogeological features of karst areas that control groundwater pollution and vulnerability.Results obtained will support decision tools aimed at the land use planning and protection of karst aquifers from pollution in karst areas.

Integrated Multi-Model Approach for Assessing Groundwater Vulnerability in Rajasthan’s Semi-Arid Zone: Incorporating DRASTIC and SINTACS Variants

Groundwater pollution in Rajasthan, India, poses significant challenges due to the region’s heavy reliance on this resource for drinking and irrigation. Given the increasing water scarcity and overexploitation, this study assesses the susceptibility of groundwater pollution in this semi-arid area. We applied and compared vulnerability mapping methods, DRASTIC and SINTACS, and their modified versions. These methodologies considered various geological and environmental factors such as depth-to-water table, recharge, aquifer conductivity, soil, and topography. The modified versions also integrated land use and temperature data for enhanced sensitivity. Validation was achieved by comparing contaminant data from the Central Ground Water Board (CGWB), India, focusing on primary contaminants such as fluoride, nitrate, chloride, and total dissolved solids (TDS). The results strongly align with the modified methodologies and observed groundwater ion values. Specifically, more than half of the 300 sample points analyzed indicated TDS values exceeding the permissible 300 ppm limit, with over 80 points surpassing 500 ppm. The vulnerability was classified into the following five categories: very low; low; medium; high; and very high. Notably, 30.53% of the area displayed “very high” vulnerability under the modified DRASTIC model. Districts like Jalore, Pali, Sirohi, and Jodhpur emerged as highly vulnerable zones, while areas within Udaipur, Kota, and Jaipur, among others, showed very high vulnerability. This research highlights the importance of conducting groundwater vulnerability assessments, especially for regions grappling with water scarcity like Rajasthan. The findings from this research are pivotal in guiding sustainable ground water resource management, as well as advocating continual monitoring and effective groundwater conservation strategies in the region.

Vulnerability and Risk of Contamination of the Varaždin Aquifer System, NW Croatia

This paper presents the first study that assesses the vulnerability and risk of contamination of groundwater in the Varaždin aquifer system. The alluvial aquifer system is mostly unconfined with favorable hydrogeological features. Out of three wellfields, two still operate at full capacity, while the Varaždin wellfield, once the major source of drinking water, has been abandoned due to high concentrations of nitrates in the groundwater. Index-based methods are employed to assess groundwater vulnerability—two DRASTIC-based methods, standard and P-DRASTIC; two SINTACS-based methods, with normal and severe weighting strings; and the GOD method. Hazard is evaluated according to recommendations from the EU COST 620 action, while the risk intensity of the resource is calculated using the results of vulnerability and hazard assessments. The results reveal that for all vulnerability models, the resulting maps have a similar distribution pattern of vulnerability classes, with the high vulnerability class prevailing. However, notwithstanding the generally high groundwater vulnerability, a moderate resource contamination risk prevails as a consequence of a dominantly low hazard index. The validation of the groundwater vulnerability models demonstrates a weak correlation between the vulnerability indices and mean nitrate concentrations in groundwater. Conversely, a significantly higher correlation coefficient (0.58) is obtained when the groundwater vulnerability index is replaced by the resource risk intensity index, indicating that the results of resource risk intensity assessments are superior to groundwater vulnerability results in predicting the level of groundwater contamination.

Assessment of groundwater vulnerable zones using conventional and Fuzzy-AHP DRASTIC for Visakhapatnam district, India

Given the increasing impact of extreme climate change and the unpredictability of monsoons, the importance of preserving groundwater resources has never been more critical. Conducting a thorough groundwater vulnerability assessment enables one to pinpoint the zones most susceptible to contamination and take proactive measures to safeguard them. This paper is an attempt to enhance the conventional DRASTIC method by integrating Fuzzy Logic and Analytical Hierarchy Processes (FAHP) and applying this approach to the Visakhapatnam district. Spatial data for the seven parameters used in this study were created and analyzed using ArcGIS. The study determines and compares the groundwater vulnerability index using both the conventional DRASTIC method and the FAHP-DRASTIC method. Based on the index values, the groundwater zones in the study area have been classified into five categories: Very Low, Low, Medium, High, and Very High Vulnerability. Model validations were conducted using the ROC-AUC technique against Nitrate concentration. The results reveal that the FAHP-DRASTIC model, with an AUC of 0.92, is the most suitable model for the present study.

Groundwater Vulnerability Assessment in Pavagada taluk, Tumakuru district, Karnataka, India, using a GIS-based DRASTIC model

Throughout the world, one of the most accessible resources is groundwater. Groundwater quality is rapidly declining due to anthropogenic and geogenic sources, resulting from over-reliance. Previous research indicates that the concentration of pollutants such as Fluoride, Arsenic, Iron, and Total Dissolved Solids has rapidly increased. This affects crop yield and human health as well as the socioeconomic development of the region. For this reason, identifying areas where groundwater is vulnerable is crucial to reducing pollution in groundwater and maintaining its quality. In this study, groundwater-vulnerable zones in Pavagada Taluk are evaluated using the GIS-based DRASTIC model. The seven DRASTIC parameters are combined with data on land use and land cover to help explain how anthropogenic sources and possible human intervention affect groundwater quality. It is observed that the groundwater samples we collected had high concentrations of Iron, Sulphate, Fluoride, and Nitrate, as per BIS 10500 (2012). The study area's northeastern, southeastern, northern, southern, and southwestern regions are classified as high to moderate risk zones on the final DRASTIC map. The high Fluoride content in groundwater is caused by bore wells situated on rocks such as Granite, Granodiorite, Hornblende-Biotite Gneiss, and Biotite. In order to validate the model, the results of the physicochemical analysis were compared to the final drastic map. This comparison shows that the model is 80.39% valid for the region.

Assessment of groundwater vulnerability to pollution in an Arid environment

Assessment of groundwater vulnerability to pollution in an Arid environment

Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

Groundwater vulnerability to pollution in degraded coal mining areas: modifying the DRASTIC method using the factor of exploitation impact on land surface

Mining activities such as underground exploitation of hard coal deposits and open cast mining are strong factors on groundwater depending on mine life cycle phases. The impact of coal mining activities on groundwater have been reported from many countries. In this case, a vulnerability assessment was conducted base on standard DRASTIC method and its modification DRASTIC MINE (DRASTICM) method. In order to take into account, the impact of mining activities, a new parameter was added which defined the impact of coal seams on the rock mass above, including the degree of its drainage and the range of its impact. In the standard DRASTIC method, the results indicate that groundwater vulnerability with high (38.6%) and very high occurrence classes (16.9% of the area), mostly covers the central part of the cast mine. In contrast, the reclaimed area of the excavation is a low-class area. The DRASTICM method increased the vulnerability index from 3 to 24 on 95% of the area, so a new vulnerability class of extremely high was delineated, which occurred in 1.6% of the area. This indicates areas that should be treated as a priority in order to avoid pollution, and in the final stage to plan activities in the field of the reclamation of mining areas. The results showed that groundwater vulnerability assessment in coal mining areas can be significantly improved.

Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach

The study try to evaluate the susceptibility of groundwater. The DRASTIC model was implemented through GIS. Various input variables, such as water table depth, net recharge, aquifer and soil media, topography, vadose zone impact, and hydraulic conductivity, were evaluated within the model to generate a groundwater vulnerability map. Subsequently, machine-learning algorithms (SVM, RF, and GLM) employed using the SDM package in R software to optimize the DRASTIC method. To assess the performance of groundwater pollution risk models, training and validation datasets were evaluated using the ROC curve. The results revealed that approximately 40% of the study area fell within the high vulnerability range, while around 30% exhibited moderate pollution risk. Evaluation of the machine learning models indicated their effectiveness in model development. The RF model demonstrated the highest predictive power, achieving an AUC of 0.98. Additionally, the GLM and SVM algorithms achieved AUC values of approximately 76%. These algorithms can serve as efficient techniques for evaluating and managing groundwater resources. The findings underscored relatively poor groundwater quality in the study area, with excessive aquifer exploitation by the agricultural sector and infiltration of urban sewage and industrial waste identified as the primary causes of groundwater pollution. The implications of these findings are crucial for devising strategies and implementing preventive measures to mitigate water resource vulnerability and associated health risks in central Iran.