Combining the classic vulnerability index and affinity propagation clustering algorithm to assess the intrinsic aquifer vulnerability of coastal aquifers on an integrated scale.

In the northern plains of Laizhou City, groundwater quality suffers dual threats from anthropogenic activities: seawater intrusion caused by overextraction of fresh groundwater, and vertical infiltration of agricultural pollutants. Groundwater management requires a comprehensive analysis of both horizontal and vertical pollution in coastal aquifers. In this paper, Intrinsic Aquifer Vulnerability (IAV) was assessed on an integrated scale using two classic IAV models (DRASTIC and GALDIT) separately based on a GIS database. Hydrogeological parameters from two classic IAV models were clustered using affinity propagation (AP) clustering algorithm, and silhouette coefficients were used to determine the optimal classification result. In our application, the objects of the AP algorithm are 3320 units divided from the whole study area with 500 m*500 m precision. A comparison of all four outputs in AP-DRASTIC shows that the clustering results of the 4-classification yielded the best silhouette coefficient of 0.406 out of all four. Cluster 4, which comprises 21% of the area, had relatively low level of groundwater contamination, despite its high level of vulnerability as indicated by the classic DRASTIC index. In the second level of vulnerability Cluster 3, 53.8% of all water samples were found to be contaminated, indicating a greater level of nitrate contamination. With respect to AP-GALDIT, the silhouette coefficient for result 7-classification reaches the highest value of 0.343. There was a high level of vulnerability identified in Clusters 2, 4 and 5 (34.7% of the study area) relating to the classic GALDIT index. The concentration of chloride in all water samples obtained in these areas was extremely high. Groundwater management should be addressed by AP-DRASTIC results on anthropogenic activity/contamination control, and by AP-GALDIT results on groundwater extraction limitation. Overall, this method allows for the evaluation of IAV in other coastal areas on an integrated scale, facilitating the development of groundwater management strategies based on a better understanding of the aquifer's essential characteristics.

Groundwater vulnerability assessment using AHP-DRASTIC-GALDIT comprehensive model: a case study of Binhai New Area, Tianjin, China.

Binhai New Area (BHNA), as one of the most economically and industrially regions in the Haihe River Basin, China, is seriously affected by seawater intrusion and groundwater over-exploitation. Groundwater vulnerability assessment (GVA) is an effective tool to protect the groundwater resources from being polluted. In this study, vertical and horizontal groundwater conditional factors were first assessed separately by two different models. The AHP-DRASTIC model was used to evaluate the intrinsic groundwater vulnerability and the AHP-GALDIT model was used to evaluate the specific groundwater vulnerability to seawater intrusion. Then, a GIS-based overlaying approach was used to get the comprehensive shallow groundwater vulnerability. The results of the comprehensive model showed that the vulnerability areas of very low, low, medium, and high account for 1.37%, 11.36%, 60.56%, and 26.71%, respectively. Finally, to effectively manage the groundwater in the study area, two remediation areas, two control areas, and one protected area were determined based on the comprehensive groundwater vulnerability maps. This study can not only promote the development of rational exploitation of shallow groundwater and prevention of groundwater pollution in BHNA but also provide a framework for future research in the GVA on the coast.

Hybridization of GALDIT method to assess actual and future coastal vulnerability to seawater intrusion.

In the recent years, the coastal aquifer of Jijel plain (North Algeria) located on the south of the Mediterranean Sea was utilized for cities growth and agricultural development of the region. Consequently, overexploitation and seawater intrusion were identified as major risks to the groundwater resource. In this work, a new approach integrating groundwater vulnerability method and numerical model for predicting the actual and future seawater is proposed. The groundwater vulnerability assessment has been performed by applying the GALDIT method using GIS and the MODFLOW model was used to simulate the actual and future groundwater level of the aquifer over the period 2020-2050. Three scenarios were simulated under water demand and climate conditions (drought, recharge) to obtain the changes in the groundwater level variation. The results of the GALDIT model application to the actual conditions (year 2020) showed that the high class of groundwater vulnerability is located in the coastal fringe and the terminal stretches of wadis where the seawater intrusion limit is located at a distance range between 840 and 1420 m from the shoreline. However, the results for predicting future groundwater vulnerability showed that the scenario which proposed the artificial recharge basins, although predicting a worrying situation compared to the actual condition, has the best figure of the groundwater vulnerability assessment and seawater intrusion despite the other two scenarios. In this case the limit in the year 2050 is located between distances of 850-1640 m from the shoreline with a forward speed of seawater intrusion of 1-8 m/year, compared to the reference year 2020. This showed that groundwater level variation and recharge were the key factors in controlling groundwater vulnerability to seawater intrusion. The presented new approach can be used to mapping the actual and future groundwater vulnerability assessment to seawater intrusion and groundwater resources management in any coastal areas worldwide.

Assessment of groundwater vulnerability to contamination: a case study.

Groundwater is a globally important, valuable resource for human life and economic development. Despite its importance, it is often misused and rarely well managed. Delineation of polluted zone attains significance especially when the pollution affects the human health. Delineation can be done by many approaches: process based, statistical and overlay and index methods. Overlay and index methods are relatively simpler and are based on hydrogeological settings and other factors which are considered to control the groundwater quality in a region. Groundwater vulnerability to contamination due to anthropogenic as well as seawater intrusion in an unconfined aquifer spread over the watershed areas of Kazhakkoottam, Kulathoor and Menamkulam regions of Thiruvananthapuram District, Kerala, India, was carried out using both susceptibility index (SI) method and modified GALDIT index method. Majority of the region selected was categorised as moderately vulnerable by both indices. The cause of groundwater vulnerability to contamination was found using factor analysis. The factor score map of the study area also shows that majority of the study area is moderately affected by contamination. Hence, the area requires continuous monitoring of groundwater quality to prevent the contamination of groundwater resources for future generations.

Integrated SEAWAT model and GALDIT method for dynamic vulnerability assessment of coastal aquifer to seawater intrusion.

Seawater intrusion (SI) has become a global issue exacerbated by intense anthropogenic activities and climate change. It is imperative to seek a synergistic strategy to reconcile environmental and economic benefits in the coastal regions. However, the intricate SI process and data scarcity present formidable challenges in dynamically assessing the coastal groundwater vulnerability. To address the challenge, this study proposed a novel framework that integrates the existing vulnerability assessment method (GALDIT) and variable-density groundwater model (SEAWAT). The future scenarios from 2019 to 2050 were investigated monthly under climate change (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and human activities (80 % and 50 % of current groundwater abstraction) in Longkou city, China, a typical coastal region subject to extensive SI, compared with the status quo in 2018. Results indicated that by 2050, the high vulnerability area, is in a narrow buffer within 1.2 km from the shoreline and exhibits minor changes while the salt concentration here increased by about 2700 mg/L compared with the current situation. The moderate vulnerability zone expands by about 30 km2, and the low vulnerable area decreases proportionally. The groundwater over-abstraction is identified as a more critical factor compared to the regional precipitation under climate change. When groundwater abstraction is reduced to 80 % of the current scale, the expansion rate of the moderate-vulnerable area slows down significantly, with an expansion area of only 18 km2 by 2050. Further reducing groundwater abstraction to 50 % of the current scale shifts the evolution trend of the medium-vulnerable area from expansion to contraction, with the area shrinking by about 11 km2 by 2050. The integrated vulnerability assessment framework can be applied not only in the similar coastal regions but also provides insights into other natural hazards.

Comparative study for coastal aquifer vulnerability assessment using deep learning and metaheuristic algorithms.

Coastal aquifer vulnerability assessment (CAVA) studies are essential for mitigating the effects of seawater intrusion (SWI) worldwide. In this research, the vulnerability of the coastal aquifer in the Lahijan region of northwest Iran was investigated. A vulnerability map (VM) was created applying hydrogeological parameters derived from the original GALDIT model (OGM). The significance of OGM parameters was assessed using the mean decrease accuracy (MDA) method, with the current state of SWI emerging as the most crucial factor for evaluating vulnerability. To optimize GALDIT weights, we introduced the biogeography-based optimization (BBO) and gray wolf optimization (GWO) techniques to obtain to hybrid OGM-BBO and OGM-GWO models, respectively. Despite considerable research focused on enhancing CAVA models, efforts to modify the weights and rates of OGM parameters by incorporating deep learning algorithms remain scarce. Hence, a convolutional neural network (CNN) algorithm was applied to produce the VM. The area under the receiver-operating characteristic curves for OGM-BBO, OGM-GWO, and VMCNN were 0.794, 0.835, and 0.982, respectively. According to the CNN-based VM, 41% of the aquifer displayed very high and high vulnerability to SWI, concentrated primarily along the coastline. Additionally, 32% of the aquifer exhibited very low and low vulnerability to SWI, predominantly in the southern and southwestern regions. The proposed model can be extended to evaluate the vulnerability of various coastal aquifers to SWI, thereby assisting land use planers and policymakers in identifying at-risk areas. Moreover, deep-learning-based approaches can help clarify the associations between aquifer vulnerability and contamination resulting from SWI.

A robust decision-making approach for designing coastal groundwater quality monitoring networks.

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3 months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer.

Incorporating hydraulic gradient and pumping rate into GALDIT framework to assess groundwater vulnerability to salinity in coastal aquifers: a case study from Urmia Plain, Iran.

The critical role of groundwater in meeting diverse needs, including drinking, industrial, and agricultural, highlights the urgency of effective resource management. Excessive groundwater extraction, especially in coastal regions including Urmia Plain in NW Iran, disrupts the equilibrium between freshwater and saline boundaries within aquifers. Influential parameters governing seawater intrusion-groundwater occurrence (G), aquifer hydraulic conductivity (A), the height of groundwater level above the mean sea level (L), distance from the shore (D), impact of the existing status of seawater intrusion (I), and thickness of the saturated aquifer (T)-merge to shape the GALDIT vulnerability index for coastal aquifers. This study enriches the GALDIT framework by incorporating two additional hydrogeological variables: hydraulic gradient (i) and pumping rate (P). This expansion produces seven distinct vulnerability maps (GALDIT, GAiDIT, GAiDIT-P, GALDIT-i, GALDIT-iP, GALDIT-P, GAPDIT). In the Urmia Plain, the traditional GALDIT index reveals vulnerability values ranging from 2 to 8.1, categorized into six classes from negligible to very high vulnerability. However, the modified indices, GAiDIT and GAiDIT-P, yield a three-class categorization, ranging from low to high vulnerability. The introduction of the "i" and "P" parameters in GALDIT-i and GALDIT-iP enhances the precision of vulnerability mapping, altering class distribution and intensifying vulnerability ratings. The eastern, central, and coastal areas of the Urmia Plain demonstrate high to very high vulnerability levels, in contrast to the lower vulnerability observed in the western regions. Both the GALDIT-P (r = 0.82) and GALDIT-iP (r = 0.81) indices show strong correlations with Cl concentration, thereby improving mapping accuracy over the traditional GALDIT index (r = 0.72). A sensitivity analysis highlights the critical influence of the "i" parameter, suggesting its weighting should be revised. Parameter recalibration serves to amplify the significance of "G," "L," "D," and "i" parameters, while diminishing others. The integration of multiple hydrogeological variables considerably enhances the precision of groundwater vulnerability assessments.

Assessing vulnerability of coastal aquifer to seawater intrusion using Convolutional Neural Network.

This study examined coastal aquifer vulnerability to seawater intrusion (SWI) in the Shiramin area in northwest Iran. Here, six types of hydrogeological data layers existing in the traditional GALDIT framework (TGF) were used to build one vulnerability map. Moreover, a modified traditional GALDIT framework (mod-TGF) was prepared by eliminating the data layer of aquifer type from the GALDIT model and adding the data layers of aquifer media and well density. To the best of our knowledge, there is a research gap to improve the TGF using deep learning algorithms. Therefore, this research adopted the Convolutional Neural Network (CNN) as a new deep learning algorithm to improve the mod-TGF framework for assessing the coastal aquifer vulnerability. Based on the findings, the CNN model could increase the performance of the mod-TGF by >30 %. This research can be a reference for further aquifer vulnerability studies.

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions.

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion.

Integrated seawater intrusion and groundwater quality assessment of a coastal aquifer: GALDIT, geospatial and analytical approaches.

The pressure and dependence on coastal aquifers are on the rise in many parts of the globe. These lead to overexploitation, aggravated levels of groundwater pollution, and seawater intrusion. Integrated analyses can create holistic insights into the quality and the vulnerability of aquifers to seawater intrusion. In this study, Mombasa North coast's coastal aquifer was characterized by integrating multiple approaches-GALDIT overlay index, seawater intrusion groundwater quality index GQISWI, total hardness, water quality index (WQI)-and the results were further explored and interpreted with geospatial analysis techniques. The study suggests that the predominant water type in areas under moderate or high vulnerabilities to seawater intrusion is the Na-Cl water type. However, similar Na-Cl water types can produce a range of total hardness from soft to hard. GQISWI classification can be used to narrow down the observations from Stuyfzand's TH-based classification system. In the aquifer studied, the results of the GALDIT overlay index, a weighted aggregation of intrinsic parameters contributing to seawater intrusion, show that 29%, 59%, and 12% of the aquifer have low, moderate, and high vulnerabilities, respectively. The GQISWI analysis indicates that the groundwater is largely brackish (68%) but saline towards the southern end of the aquifer at 32%. Total hardness values indicate that 67% of the aquifer's coverage falls under the "moderately hard" category. The geodatabase creation introduced in the study provides a template for similar studies and a baseline for future WQI and water quality monitoring. However, temporal studies on chronological timescales are recommended for sustainable management of the aquifer.

New hybrid-based approach for improving the accuracy of coastal aquifer vulnerability assessment maps.

Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivity of the identification of rates and weights. This study aimes at developing a new hybrid framework for improving the accuracy of coastal aquifer vulnerability assessment using various statistical, metaheuristic, and Multi-Attribute Decision Making (MADM) methods to improve the GALDIT model. The Gharesoo-Gorgan Rood coastal aquifer in northern Iran is used as study site. In order to meet this aim, the Differential Evolution (DE) and Biogeography-Based Optimization (BBO) metaheuristic algorithms were employed to optimize the GALDIT weights. In addition, a novel MADM method, named Step-wise Weight Assessment Ratio Analysis (SWARA), and the bivariate statistical method called statistical index (SI) were used to modify the GALDIT ratings. Finally, correlation coefficients between the maps obtained from each method and Total Dissolved Solid (TDS) as an indicator of seawater intrusion were computed to evaluate the models' prediction power. Correlation coefficients of 0.72, 0.75, 0.76 and 0.78 were obtained for the GALDITSWARA-BBO, GALDITSI-BBO, GALDITSWARA-DE and GALDITSI-DE models, respectively. The results from the GALDITSI-DE model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDITSI-DE model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.

Assessment of groundwater vulnerability using integrated remote sensing and GIS techniques for the West Bengal coast, India.

Groundwater in the coastal districts of West Bengal, India is highly susceptible to various factors such as over-pumping, variations in rainfall, lower elevation and risk due to sea level rise. In addition to these factors, tropical cyclone induced storm surge and saltwater intrusion also induce potential risks to the quality of the coastal aquifers. There are several knowledge gaps, as many of these factors have not previously been systematically and rigorously analysed; furthermore, up-to-date information is either unavailable or insufficient. Accordingly, the present study analysed the groundwater vulnerability during the pre- and post-monsoon months for the period from 2001 to 2010 at three main coastal districts of West Bengal: East Midnapore, South 24 Parganas and North 24 Parganas (administrative regions). The GALDIT index-based model was employed to assess salt-water intrusion into the groundwater using Geographic Information System (GIS). Spatial distribution maps were also generated to identify highly vulnerable groundwater locations. Map removal and single parameter sensitivity analyses were performed to understand the sensitivity of the parameters. The study reveals that the depth of ground-water levels for the three districts increased and also the rainfall exerts a significant effect on the groundwater depth. The chemical constituents TDS and chloride contents in groundwater during the period 2004 to 2010 were analysed. The average TDS range values for pre- and post-monsoon seasons were observed to vary in the range between 100 and 3874 mg/l and 83-1929 mg/l respectively. Reports indicate that, groundwater in the area is highly saturated with iron containing minerals like Fe(OH)3, goethite, and hematite and is also moderately saturated with the calcite, chalcedony, dolomite and quartz, whereas under-saturated with anhydrite and gypsum. The implications of the research points to the urgent need for remedial action and appropriate responses at policy-level to protect groundwater.

An optimization method coupled the index-overlay method with entropy weighting model to assess seawater intrusion vulnerability.

Seawater intrusion poses a serious threat to coastal areas around the world. The purpose of this study was to develop a comprehensive approach to assess the vulnerability of saltwater intrusion. The powerful decision-making technique GALDIT was firstly selected, and its inherent weights are the origin of the subjective method. The entropy method was then integrated to reasonably determine the objective weight of this basic structure. Furthermore, to balance conflicts between subjective and objective methods, game theory was intruded upon. The result of the sensitivity analysis showed a correlation coefficient between the effective weights and theoretical weights of the normal method, entropy theory, and game theory of 0.66, 0.89, and 0.94, respectively. Meanwhile, the best correlation coefficient between the vulnerability indices and the values of 38 monitoring wells was obtained by the game model. Finally, the optimal weights of G, A, L, D, I, and T were 0.096, 0.153, 0.220, 0.320, 0.150, and 0.061, respectively. The study area was finally classified into regions with high, moderate, and low vulnerability, accounting for 11.4%, 24.9%, and 63.7% of the area. The paper included that the optimization of GALDIT through game theory gives a more accurate assessment of the groundwater vulnerability to seawater intrusion.

A new hybrid framework for optimization and modification of groundwater vulnerability in coastal aquifer.

Effects of pollution caused by seawater intrusion into groundwater in coastal aquifers cannot be ignored. Identification of areas exposed to this pollution by preparing vulnerability maps is one way of preventing aquifer pollution. In its primary section, the present study compared three different index ranking methods of DRASTIC, GALDIT, and SINTACS to select an optimal model for determining vulnerability of the Gharesoo-Gorgan Rood coastal aquifer. Initial results led to selection of the GALDIT model for vulnerability assessment of the selected coastal aquifer. Since this type of models use a rating system, the model must be modified and optimized in various regions to show the vulnerable areas more accurately. In the next step, and for the first time, the ratings in this index were modified using the Wilcoxon nonparametric statistical method and its weights were optimized employing particle swarm optimization (PSO) and single-parameter sensitivity analysis (SPSA) methods. Finally, in order to select the best hybrid model, the total dissolved solids (TDS) parameter was used to determine correlation coefficients. Results indicated that the GALDT model modified by the Wilcoxon-PSO method has the strongest correlation (0.77) with the TDS parameter. Moreover, the correlations of the Wilcoxon-GALDIT and Wilcoxon-SPSA models were 0.66 and 0.73, respectively. Final results of the Wilcoxon-PSO model revealed that the northwestern and western areas of the study region needed considerable protection against pollution. In general, we can conclude that by combining statistical, mathematical, and metaheuristic methods, we can obtain more accurate results for preparing vulnerability maps.

Seawater intrusion vulnerability in the coastal aquifers of southern India-an appraisal of the GALDIT model, parameters' sensitivity, and hydrochemical indicators.

An appraisal of seawater intrusion into the coastal aquifers is one of the major issues for groundwater resource management. The GALDIT model applies to the analysis of multiple parameters using systematic GIS techniques for mapping and assessment of seawater intrusion vulnerability. It demarcates the mapping of potential vulnerability that shows a higher vulnerability to seawater intrusion in various parts of the coast and the estimated vulnerability index value of 7.50 and 9.64. An area of 33.0 km2 spread in the low-lying coastal area comprising estuaries, salt marshes, and saltpans shows the high vulnerability condition with an estimated vulnerability value of 6.42-7.50. An area of 73.20 km2 spread over coastal and alluvial plains experiences moderate vulnerability (temporal salinity in the groundwater sources) with an estimated vulnerability index value of 5.46-6.42. Aquifers underlying coastal uplands (hard rock formations) and some parts of accretionary beaches (2.05 km2) are relatively protected fresh groundwater sources, wherein the estimated vulnerability index is 4.55-5.46. The vulnerability mapping of the GALDIT model using hydrochemical analysis of primary groundwater parameters such as TDS, Cl-, HCO3, and Cl-/HCO3 ratio is validated. Higher concentration of TDS (2637-4162 mg/l) and Cl- (1268-2347 mg/l) is taken for the areas falling under higher vulnerability to seawater intrusion, especially in the placer mining sites and coastal areas facing erosion. Similarly, the groundwater sources of the low-lying areas including estuaries, salt marshes, saltpans, and backwater were noted to have higher values of Cl-/HCO3 with a rationality of 9.87-12.18. Hydrological facies shows the highest concentration of NaCl in the groundwater sources within the proximity of eroded beaches, saltwater bodies, and sand mining areas. A hydrochemical facies evolution (HFE) diagram represents the hydrochemical facies of groundwater elements that shows an intrusion of seawater into the coastal aquifers underlying the very high vulnerable zones. Higher bicarbonate concentration (233-318 mg/l) is noticed in the upland areas and some parts of dunes and accreted beaches, sandy coasts, and uplands. Vulnerability analysis reveals that those areas near saltwater bodies and eroding coasts are prone to lateral and vertical diffusion of saltwater. The geodatabase developed through such modeling studies can help in planning and developing activities for sustainable groundwater resource management in coastal areas.

Groundwater vulnerability assessment using the GALDIT model and the improved DRASTIC model: a case in Weibei Plain, China.

This study employed a modified DRASTIC model (AHP-DRASTIC model) and GALDIT model to evaluate the inherent vulnerability of shallow groundwater in Weibei Plain in Shandong Province of China and its vulnerability to seawater intrusion. The AHP-DRASTIC model uses the analytic hierarchy process (AHP) to determine the weight of each parameter and reduces the subjectivity of evaluation. The vulnerability map generated by the AHP-DRASTIC model shows four types of vulnerability: high (25.0%), higher (28.0%), moderate (29.7%), and low (17.3%), and the high-vulnerability areas are mainly distributed in the area north of Qingxiang Town and south of Changyi County. The distribution of high-vulnerability areas mainly related to the depth of groundwater table is 4-8 m, and the recharge of rainfall is 100-175 mm/year. The vulnerability map generated by the GALDIT model shows four types of vulnerability: high (33.5%), higher (23.4%), moderate (22.1%), and low (21.0%), and the high-vulnerability areas are mainly distributed in the coastal areas of Hanting District-Zhuli Town, the areas north of Linqu County, and the areas south of Shouguang County. The distribution of high-vulnerability areas mainly related to the distance between these areas and the coast is < 2.5 km, with aquifer thickness > 15 m. Total dissolved solid, NO3-, Cl-, and SO42- are used to verify the accuracy of the DRASTIC model, the AHP-DRASTIC model, and the GALDIT model. The results show that the AHP-DRASTIC model is more suitable for the assessment of inherent vulnerability of shallow groundwater in the study area than the DRASTIC model, and human activities have a major impact on the verification of vulnerability and should be considered when conducting groundwater vulnerability verification. The results of this study can provide grounds for groundwater management and protection and land use planning in the study area and provide new ideas for groundwater vulnerability assessment in coastal areas.