Groundwater vulnerability assessment: A review including new statistical and hybrid methods

Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis

Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management.

Temporal evaluation of seawater intrusion vulnerability in Shabestar Plain using GALDIT and AI techniques

Groundwater contamination, with seawater intrusion (SWI) being the most widespread form particularly in coastal areas, has become a pressing global environmental challenge. Groundwater serves as a vital freshwater resource, particularly in arid and semi-arid regions, making its efficient management essential. In this study, the GALDIT method-an index-based approach that evaluates the vulnerability of aquifers by scoring six key parameters based on expert judgment (groundwater occurrence (G), aquifer hydraulic conductivity (A), groundwater elevation above sea level (L), distance from the shoreline (D), impact of existing seawater intrusion (I), and aquifer thickness (T))-was employed to assess the vulnerability of the Shabestar aquifer to SWI. The study employs the GALDIT method to map aquifer vulnerability for 2002, 2012, and 2022, enabling a temporal comparison of changes over time. The final GALDIT index map, categorized into low, moderate, and high vulnerability classes, revealed an increase in very high vulnerability areas from 10.9% in 2002 to 17.8% in 2022, alongside a decrease in moderate vulnerability areas from 56.4 to 37.3%, indicating a deteriorating condition of the aquifer. However, the reliance on expert judgment introduces potential subjectivity and bias in the vulnerability assessment. To mitigate these limitations, AI-based models, namely artificial neural networks (ANNs) and random forest (RF), were applied to enhance model performance. The GALDIT parameters served as input for the AI models, while observed electrical conductivity (EC), a key indicator of water salinity, and total dissolved solids (TDS), an indicator of drinking water quality, were used as output variables to estimate condition for the year 2022. Results demonstrated that the ANN model outperformed the RF model during verification, improving estimation accuracy by up to 10% and 9% in terms of the determination coefficient (DC), respectively. To further enhance model interpretability and identify the most influential parameters for EC and TDS estimation, a global, variance-based sensitivity analysis using the Sobol method was conducted. This analysis revealed that factors I and D were the most influential for EC, while factors I and T had the greatest impact on TDS in the study region.

Enhancing groundwater quality prediction through ensemble machine learning techniques

Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensemble learning model, for predicting groundwater quality in an unconfined aquifer located in northern Iran. The groundwater quality index (GWQI) from 250 wells was evaluated and classified. We considered various influential factors such as proximity to residential areas, evaporation, aquifer transmissivity, precipitation values, population density, distance to industrial centers, distance to water resources, and topography. Three different ML classifiers were employed to establish relationships between GWQI and the aforementioned factors: the AdaBoost classifier (ADA), quadratic discriminant analysis (QDA), and stacking ensemble learning (SEL). A novel model was introduced dubbed quadratic-ada-stacking ensemble learning (QA-SEL) to predict GWQI. The performance of these algorithms was evaluated through the receiver-operating characteristic (ROC) and multiple statistical efficiency indicators, including overall accuracy, precision, recall, and the F-1 score. All three ML algorithms displayed a high degree of accuracy in their GWQI predictions. Nonetheless, the QA-SEL method was identified as the most effective model due to its superior accuracy (overall accuracy, precision, recall = 0.95, 0.95, 0.96, ROC = 0.96, respectively). Following model optimization and testing, the QA-SEL model and a GIS were employed to map GWQI classes across the entire area. The produced GWQI map was validated by comparing the measured and predicted GWQI on the map. This study offers an economically efficient model for groundwater quality prediction, which can be replicated in other plains.

Mapping key areas to protect high-value and high-vulnerability groundwater from pollution load: Method for management

Severe groundwater pollution has necessitated prioritizing the prevention and control of groundwater pollution (PCGP). The fundamental strategy of PCGP involves identifying priority areas. Vulnerability assessment, such as DRASTIC, and its extension, pollution risk assessment, have been developed to guide PCGP. However, managers find it struggling to implement these results in PCGP due to a lack of consideration for practical management demands. This study establishes a management-oriented method to map key areas for groundwater protection and PCGP, considering water sources, pollution source load, vulnerability, and function value, to facilitate management implementation. The key area includes the protection area aimed at protecting water sources and the control area focused on preventing and controlling pollution load in high-value and high-vulnerability groundwater. The effectiveness and practicality of this method are demonstrated through a case study in a large district reliant on groundwater, enabling the key area and corresponding suggestions to directly guide local management. This method offers a practical tool for PCGP worldwide and is expected to guide the sustainable development plan.

Improving groundwater vulnerability assessment in structurally controlled hard rock aquifer: insight from lineament density and land use/land cover pattern

A comprehensive seasonal assessment of groundwater vulnerability was conducted in the weathered hard rock aquifer of the upper Swarnrekha watershed in Ranchi district, India. Lineament density (Ld) and land use/land cover (LULC) were integrated into the conventional DRASTIC and Pesticide DRASTIC (P-DRASTIC) models and were extensively compared with six modified models, viz. DRASTIC-Ld, DRASTIC-Lu, DRASTIC-LdLu, P-DRASTIC-Ld, P-DRASTIC-Lu, and P-DRASTIC-LdLu, to identify the most optimal model for vulnerability mapping in hard rock terrain of the region. Findings were geochemically validated using NO3- concentrations of 68 wells during pre-monsoon (Pre-M) and post-monsoon (Post-M) 2022. Irrespective of the applied model, groundwater vulnerability shows significant seasonal variation, with > 45% of the region classified as high to very high vulnerability in the pre-M, increasing to ̴67% in post-M season, highlighting the importance of seasonal vulnerability assessments. Agriculture and industries' dominant southern region showed higher vulnerability, followed by regions with high Ld and thin weathered zone. Incorporating Ld and LULC parameters into DRASTIC-LdLu and P-DRASTIC-LdLu models increases the 'Very High' vulnerability zones to 17.4% and 17.6% for pre-M and 29.4% and 27.9% for post-M, respectively. Similarly, 'High' vulnerable zones increase from 32.5% and 25% in pre-M to 33.8% and 35.3% in post-M for respective models. Model output comparisons suggest that modified DRASTIC-LdLu and P-DRASTIC-LdLu perform better, with accurate estimations of 83.8% and 89.7% for pre-M and post-M, respectively. However, results of geochemical validation suggest that among all the applied modified models, DRASTIC-LdLu performs best, with accurate estimations of 34.4% and 20.6% for pre-M and post-M, respectively.

Mapping and assessment of groundwater pollution risks in the main aquifer of the Mostaganem plateau (Northwest Algeria): utilizing the novel vulnerability index and decision tree model

Water plays a pivotal role in socio-economic development in Algeria. However, the overexploitations of groundwater resources, water scarcity, and the proliferation of pollution sources (including industrial and urban effluents, untreated landfills, and chemical fertilizers, etc.) have resulted in substantial groundwater contamination. Preserving water irrigation quality has thus become a primary priority, capturing the attention of both scientists and local authorities. The current study introduces an innovative method to mapping contamination risks, integrating vulnerability assessments, land use patterns (as a sources of pollution), and groundwater overexploitation (represented by the waterhole density) through the implementation of a decision tree model. The resulting risk map illustrates the probability of contamination occurrence in the substantial aquifer on the plateau of Mostaganem. An agricultural region characterized by the intensive nutrients and pesticides use, the significant presence of septic tanks, widespread illegal dumping, and a technical landfill not compliant with environmental standards. The critical situation in the region is exacerbated by excessive groundwater pumping surpassing the aquifer's natural replenishment capacity (with 115 boreholes and 6345 operational wells), especially in a semi-arid climate featuring limited water resources and frequent drought. Vulnerability was evaluated using the DRFTID method, a derivative of the DRASTIC model, considering parameters such as depth to groundwater, recharge, fracture density, slope, nature of the unsaturated zone, and the drainage density. All these parameters are combined with analyses of inter-parameter relationship effects. The results show a spatial distribution into three risk levels (low, medium, and high), with 31.5% designated as high risk, and 56% as medium risk. The validation of this mapping relies on the assessment of physicochemical analyses in samples collected between 2010 and 2020. The results indicate elevated groundwater contamination levels in samples. Chloride exceeded acceptable levels by 100%, nitrate by 71%, calcium by 50%, and sodium by 42%. These elevated concentrations impact electrical conductivity, resulting in highly mineralized water attributed to anthropogenic agricultural pollution and septic tank discharges. High-risk zones align with areas exhibiting elevated nitrate and chloride concentrations. This model, deemed satisfactory, significantly enhances the sustainable management of water resources and irrigated land across various areas. In the long term, it would be beneficial to refine "vulnerability and risk" models by integrating detailed data on land use, groundwater exploitation, and hydrogeological and hydrochemical characteristics. This approach could improve vulnerability accuracy and pollution risk maps, particularly through detailed local data availability. It is also crucial that public authorities support these initiatives by adapting them to local geographical and climatic specificities on a regional and national scale. Finally, these studies have the potential to foster sustainable development at different geographical levels.

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.

Theoretical analysis and considerations of the main parameters used to evaluate intrinsic karst groundwater vulnerability to surface pollution

Karst aquifers are highly susceptible to surface pollution scenarios due to exokarst features allowing a fast infiltration regime, bypassing the unsaturated zone. Intrinsic vulnerability maps are a visual interpretation of different levels of vulnerability estimated from multiple arrays of natural characteristics of the aquifer. However, for karst aquifers, this type of analysis is affected by the high subjectivity and personal interpretations of some karst features from hydrological or geological points of view. Current methodologies to assess groundwater vulnerability in karst differ in the number and type of evaluated parameters; they have unsimilar rates, weights, and sometimes a contradictory evaluation of some karst features' hydrogeological behaviour. This paper reviews the main parameters utilized to obtain intrinsic vulnerability maps, including their rating and weighting process, in order to provide additional insights and assist on the process of groundwater vulnerability analysis. After the review of twelve methodologies' guidelines and their application on 45 study areas around the world, new considerations to evaluate parameters and the assignation of rates and weights, according to infiltration scenarios, are here proposed.

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 modified DRASTIC model for groundwater vulnerability assessment using connecting path and analytic hierarchy process methods

Groundwater plays a vital role in supporting water for the different needs of domestic, agricultural, and industrial sectors, and its vulnerability assessment to pollution is a valuable tool for establishing protective and preventive management. DRASTIC is a well-known GIS-based model for assessing groundwater vulnerability to pollution, which uses seven parameters including depth-to-water level, net recharge, aquifer media, soil media, topography, the impact of the vadose zone, and hydraulic conductivity. The predefined weights of DRASTIC parameters have made a barrier to its applicability for different regions with different hydroclimatic conditions. To overcome this problem, it has been suggested to apply analytic hierarchy process (AHP) method for modifying the model by adjusting the weights of the parameters. AHP is a widely used method to elicit experts' judgments about different involving parameters through constructing pairwise comparison matrixes (PCMs). Since AHP calculates the weights by performing pairwise comparisons between the parameters, achieving consistent comparisons is difficult when the number of parameters increases. The objective of this research is to modify the DRASTIC model by integrating the connecting path method (CPM) and AHP. The proposed methodology involves asking experts to perform a number of pairwise comparisons between the parameters and then construct an incomplete PCM using the obtained information. To complete the missing values in the PCM, CPM is employed. The CPM is an effective approach that not only estimates missing judgments but also ensures minimal geometric consistency. The proposed method along with DRASTIC and pesticide DRASTIC models is applied to Khoy County, which is located in the northwest part of Iran. The efficiency of the proposed method was further confirmed through the results of the Pearson coefficient test conducted on nitrate concentrations. The test revealed correlation values of 0.47, 0.27, and 0.57 for DRASTIC, pesticide DRASTIC, and modified DRASTIC, respectively. These results demonstrated that the proposed method provides a more precise evaluation of groundwater vulnerability.

Implementation and evaluation of different techniques to modify DRASTIC method for groundwater vulnerability assessment: a case study from Bouficha aquifer, Tunisia

Groundwater vulnerability assessment has nowadays evolved into an essential tool towards proper groundwater protection and management, while the DRASTIC method is included among the most widely applied vulnerability assessment methods. However, the high uncertainty of the DRASTIC method mainly associated with the subjectivity in assigning parameters ratings and weights has driven many researchers to apply various methods for improving its efficiency. In this context, in the present study, different techniques were implemented with the aim of modifying the DRASTIC framework and thus enhancing its performance for groundwater vulnerability assessment in the Bouficha aquifer, Tunisia. In a first stage, the land use type (L) was incorporated as an additional parameter in the typical DRASTIC framework, thus taking into consideration the impact of anthropogenic activities on groundwater vulnerability. Subsequently, the rating and weighting systems of the developed DRASTIC-L framework were modified through the application of statistical methods (DRASTIC-L-SA) and genetic algorithms (GA) (DRASTIC-L-GA) in an attempt to investigate and compare both linear and nonlinear modifications. To evaluate the various vulnerability frameworks, correlation between vulnerability values and nitrate concentrations, expressed as Spearman's rank correlation coefficient (ρ) and Correlation Index (CI), was examined. The results revealed that the DRASTIC-L-GA framework developed by applying a fully GA-based optimization procedure provided the highest values in terms of the performance metrics used, making it the most suitable for the study area. In addition, the aquifer under study was found to be less vulnerable to pollution when employing the typical DRASTIC framework instead of the modified ones, leading to the conclusion that the former substantially underestimates pollution potential in the study area.

Assessment of groundwater vulnerability by applying the improved DRASTIC model: a case in Guyuan City, Ningxia, China

Groundwater is the main source of production and living in most arid and semi-arid areas, and it plays an increasingly critical role in achieving local urban development. There is a serious issue regarding the contradiction between urban development and groundwater protection. In this study, we used three different models to assess the groundwater vulnerability of Guyuan City, including DRASTIC model, analytical hierarchy process-DRASTIC model (AHP-DRASTIC) and variable weight theory-DRASTIC model (VW-DRASTIC). The groundwater vulnerability index (GVI) of the study area was calculated in ArcGIS. Based on the magnitude of GVI, the groundwater vulnerability was classified into five classes: very high, high, medium, low, and very low using the natural breakpoint method, and the groundwater vulnerability map (GVM) of the study area was drawn. In order to validate the accuracy of groundwater vulnerability, the Spearman correlation coefficient was used, and the results showed that the VW-DRASTIC model performed best among the three models (ρ=0.83). The improved VW-DRASTIC model shows that the variable weight model effectively improves the accuracy of the DRASTIC model, which is more suitable for the study area. Finally, based on the results of GVM combined with the distribution of F^- and urban development planning, suggestions were proposed for further sustainable groundwater management. This study provides a scientific basis for groundwater management in Guyuan City, which can be an example for similar areas, particularly in arid and semi-arid areas.

Novel machine learning algorithms to predict the groundwater vulnerability index to nitrate pollution at two levels of modeling

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO₃-N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination.

Comparison of DRASTIC and DRASTICL groundwater vulnerability assessments of the Burdekin Basin, Queensland, Australia

In the Burdekin Basin, Queensland, Australia, groundwater contamination due to agricultural activities has led to concerns over its impacts on globally significant ecosystems such as the Great Barrier Reef. An appropriate method for groundwater vulnerability assessment is essential for the sustainable use of this groundwater resource and its longer-term environmental management. The aim of this study is to apply and assess the suitability of the standard DRASTIC index-based method for groundwater vulnerability assessment of the Burdekin Basin. The intrinsic groundwater vulnerability is calculated in ArcGIS, using data for the period 2010 to 2021. The results are compared to available water quality data. The calculated DRASTIC scores are found to be only weakly correlated with water quality parameters, including the nitrate concentration (R = 0.07), which should behave as a proxy measure of groundwater vulnerability. To address this, a modified DRASTICL method containing a land use parameter is also implemented, to assess the specific groundwater vulnerability. The correlation between DRASTICL scores and nitrate levels (R = 0.2) is more significant but is still relatively weak. From this study, it is recommended that alternative methods be developed to assess groundwater vulnerability in the Burdekin Basin, and other comparable aquifer systems.

Experiment on monitoring leakage of landfill leachate by parallel potentiometric monitoring method

The accumulation of municipal solid waste (MSW) in landfills often becomes a serious pollution source of geological environment and groundwater. The geological environment is the carrier of the landfill, and also the main pollution object of the landfill. The main pollution modes of the landfill site to the surrounding geological environment are purging, flushing, leachate, etc. If the leachate leakage cannot be found and repaired in time, it will cause serious harm to the geological environment and groundwater. The cost of geological environment and groundwater sampling through borehole surveys is high. Therefore, monitoring the seepage path and migration law of leachate is of great significance for determining the pollution range of the landfill site. In this study, by adjusting the grids of different sizes and changing the flow rate of leachate, the monitoring of fluid migration of different types of leachate was strengthened. The results show that the parallel potential monitoring method can quickly reflect the location and number of leachate points and the migration law of leachate. It provides effective reference data for landfill leachate monitoring.

Comparative assessment of groundwater vulnerability using GIS-based DRASTIC and DRASTIC-AHP for Thoothukudi District, Tamil Nadu India

The groundwater is very precious in the world. Rapid urbanization and industrialization create tremendous stress on groundwater quality and quantity. Unscientific groundwater extraction and waste disposal methods impact the groundwater aquifer's susceptibility in the coastal area. This research examines how industrial waste, seawater intrusion, and solid waste dumping affect the Thoothukudi District, located on the southwest coast of Tamil Nadu, India. The groundwater vulnerability potential is determined using the DRASTIC and analytical hierarchy process (AHP)-based DRASTIC model. DRASTIC-AHP method's weights and ranks are determined using multi-criteria decision analysis (MCDA)-based pairwise comparison method. Remote sensing (RS) and geographical information system (GIS) are implemented to prepare the input layers for DRASTIC and DRASTIC-AHP. The findings reveal a very high category of vulnerability along the coastline that is covered in sand and loose sediments from an aquifer. Similar conditions exist on the southeast side, which is covered with gravel, sand, and sandstone with shale and has relatively low-slope topography. This enables higher contaminant percolation into the groundwater and raises the possibility for pollution. The DRASTIC-AHP method's results reveal that the southeast side has a significant possibility of contamination. The water table, net recharge, vadose zone, and conductivity greatly impacted the DRASTIC vulnerability assessment due to their stronger weight than theoretical weight. It may be stated that the DRASTIC technique is more cost-effective and time-efficient in analyzing a wide range of regional groundwater risks while avoiding sloppy, uncontrolled land development and other unwanted activities.

GALDIT Modification for Seasonal Seawater Intrusion Mapping Using Multi Criteria Decision Making Methods

Recently, coastal aquifers have been found to be increasingly exposed to seawater intrusion (SWI) due to climate change and anthropogenic activities. Various method exists for coastal aquifer vulnerability mapping and the one most commonly used is GALDIT because of its simplicity. The present study modified the original GALDIT ratings and weights using Shannon’s entropy theory to study the seasonal vulnerability of coastal aquifer in the coastal region of Benin, West Africa. Thus, the monthly GALDIT index for the study region was computed using 5 years of (2015–2019) average data of GALDIT dynamic input parameters. The original and modified GALDIT approaches were validated using total dissolved solid (TDS) concentration. Pearson’s correlation and Spearman coefficient correlations were calculated, and generally the modification of the GALDIT parameters’ relative weight using entropy has improved the method as this gave a better correlation with TDS concentration (0.739). From the calculated monthly GALDIT index, the most vulnerable period was identified using TOPSIS method. Based on TOPSIS results, the coastal aquifer of Benin is more vulnerable to seawater intrusion in February due to the decrease of groundwater level in that period and less vulnerable in July. The performed sensitivity analysis showed that height of groundwater level above the mean sea level, distance from shore, and thickness of the saturated aquifer have the most influence in vulnerability to SWI assessment in the study area.

Groundwater Vulnerability Assessment in the Metaponto Coastal Plain (Basilicata, Italy)

This study aims at a groundwater vulnerability assessment of the Metaponto coastal plain, located in the Basilicata region (southern Italy). In the last century, intensive agriculture, zootechnical and industrial activities have significantly changed the plain. These changes led to negative impacts on the hydrogeological system intensifying the risk of the aquifer to pollution. The paper presents the assessment of the intrinsic vulnerability of the coastal aquifer carried out by the GIS-based application of the SINTACS method. It considers several aquifer parameters such as water table depth, effective infiltration, unsaturated conditions, soil media, aquifer media, hydraulic conductivity and topography. Furthermore, the anthropogenic influence in the study area was considered by applying the SINTACS-LU method, in which the parameter of land use (LU) was added. The SINTACS and SINTACS-LU vulnerability indexes were provided by summing the product of ratings and weights assigned to each parameter. The analysis of the intrinsic vulnerability map allowed for determining three classes ranging from low to high vulnerability. In both cases, the southeastern part of the coastal plain, closest to the sea, shows the highest vulnerability class, indicating that it is the most vulnerable to contamination due to the hydrogeological intrinsic factors. The wide central part of the study area shows a moderate class of vulnerability and the low class is scattered in small parts in the northern portion of the plain, which represents the areas less contaminable in space and time in the case of potential pollution. In the SINTACS-LU map, some areas classified as highly vulnerable in the SINTACS method show a minor vulnerability class. These areas are localized in natural and wooded sectors of the Metaponto plain, which are less populated, where human impact on the groundwater is minimal.