Integrated approach to understand the multiple natural and anthropogenic stresses on intensively irrigated coastal aquifer in the Mediterranean region.

Understanding the major factors influencing groundwater chemistry and its evolution in irrigation areas is crucial for efficient irrigation management. Major ions and isotopes (δD-H2O together with δ18O-H2O) were used to identify the natural and anthropogenic factors contributing to groundwater salinization in the shallow aquifer of the Wadi Guenniche Plain (WGP) in the Mediterranean region of Tunisia. A comprehensive geochemical investigation of groundwater was conducted during both the low irrigation season (L-IR) and the high irrigation season (H-IR). The results show that the variation range and average concentrations of almost all the ions in both the L-IR and H-IR seasons are high. The groundwater in both seasons is characterized by high electrical conductivity and CaMgCl/SO4 and NaCl types. The dissolution of halite and gypsum, the precipitation of calcite and dolomite, and Na-Ca exchange are the main chemical reactions in the geochemical evolution of groundwater in the Wadi Guenniche Shallow Aquifer (WGSA). Stable isotopes of hydrogen and oxygen (δ18O-H2O and δD-H2O) indicate that groundwater in WGSA originated from local precipitation. In the H-IR season, the δ18O-H2O and δD-H2O values indicate that the groundwater experienced noticeable evaporation. The enriched isotopic signatures reveal that the WGSA's groundwater was influenced by irrigation return flow and seawater intrusion. The proportions of mixing with seawater were found to vary between 0.12% and 5.95%, and between 0.13% and 8.42% during the L-IR and H-IR seasons, respectively. Irrigation return flow and the associated evaporation increase the dissolved solids content in groundwater during the irrigation season. The long-term human activities (fertilization, irrigation, and septic waste infiltration) are the main drives of the high nitrate-N concentrations in groundwater. In coastal irrigation areas suffering from water scarcity, these results can help planners and policy makers understand the complexities of groundwater salinization to enable more sustainable management and development.

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques.

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy.

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

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.

Inferences on metal pollution in the natural spawning zone of Bangladesh river and pollution management strategies.

The purpose of this study was to evaluate the metal concentrations in the Halda River in Bangladesh to determine the quality of the water and sediment in the natural spawning zone. Fe > Zn > Cr > Cd > Cu was the order of the metals in water, whereas Fe > Zn > Cd > Cu was the order in sediments. Almost all of the heavy metals in the water and sediment had been found within the established limits, with the exception of Cr and Fe in the river and Cu in the sediment. In the case of water, Cr vs. Zn was found to have the strongest correlation (r = 0.96). Due to the coagulation and adsorption processes, it was shown that Fe and Zn had a substantial correlation of 0.96, Cu and Cd of 0.91, and Cr of 0.78 with Zn. Hazard quotient values of Cd show the not potable nature of Halda river surface water and might give adverse health effects for all age groups except Cu and Zn. Pollution load index values indicated the uncontaminated nature of the river bottom sediments. Natural and human activities were the key factors influencing the accumulation and movement of heavy metals in the water and sediments. Contamination sources are industrial effluents, garbage runoff, farming operations, and oil spills from fishing vessels which are comparable according to multivariate statistical analysis. Ion exchange, absorption, precipitation, complexation, filtration, bio-absorption, redox reaction, and reverse osmosis were considered to be effective for the degradation of metal concentrations. The feasibility of the suggested metal reduction procedures has to be studied to know which is optimally appropriate for this river region. It is expected that this study could provide a useful suggestion to decrease the metal pollution in the river.

A review on global status of fresh and saline groundwater discharge into the ocean.

Submarine groundwater discharge (SGD) is the groundwater flow from land to the sea across the seabed, and it includes both terrane freshwater and recirculated seawater in the sub-surface. This review (i) systematically evaluates findings of various quantification methodologies, (ii) examines the estimated SGD in scientific publications between 2000 and 2020, and (iii) quantitatively evaluates current situation of coastal zone management through the bibliometric analysis of research papers. Apart from enhancing the shortage of groundwater resources in coastal area, the SGD brings nutrients (nitrate and phosphate), toxic heavy metals, and organic compounds, and thus contaminate the seawater. Therefore, the improved understanding about location and quantity of global SGD is essential to conserve the coastal and ocean ecosystems.

Submarine groundwater discharge and associated nutrient influx in surroundings of the estuary region at Gulf of Mannar coast, Indian Ocean.

The influx of fresh groundwater and re-circulated sea water into coastal ecosystem occurs through the submarine groundwater discharge (SGD). Measurement of salinity, radium tracers (224Ra, and 226Ra isotopes) and nutrients in estuarine water, coastal surface water and groundwater during December 2019 estimated the SGD and associated nutrient fluxes near the Karameniyar estuary (Gulf of Mannar) and surroundings of the Manapad region at southern part of Tamil Nadu state in India. The presence of excessive radium tracers revealed that the SGD was contributing to Ra desorption from the sediments and enrichment in the coastal waters. We estimated SGD of approximately 0.03-0.59 m3 m-2 d-1 for the Manapad region and relatively more homogeneous but comparatively less values in the Karameniyar estuary (0.03-0.34 m3 m-2 d-1). Higher average values of dissolved inorganic nitrogen (DIN; 43.62 µmol L-1) and soluble reactive phosphate (SRP; 1.848 µmol L-1) suggested greater influence of SGD on the overall coastal water nutrient budget. This study also indicated simultaneous occurrence of fresh and saline SGD in this region.

Characteristics of microplastics in the beach sediments of Marina tourist beach, Chennai, India.

Marina beach in Chennai metropolitan city attracts numerous tourists from all around the world, and it is an important ecological habitat for many life forms. Rapid urbanisation and industrial developments have led to excessive use of plastics and increased the amount of plastic waste generated in the natural environment. This first baseline study evaluates the microplastic (MP) accumulation in beach surface sediments of Marina and Pattinapakkam beaches through FTIR, AFM and SEM analyses. Sediment samples were collected from 40 stations and different types of MP polymers were identified. On average, 459 (60.8%) and 297 (39.2%) MP particles were found in the samples from Marina and Pattinapakkam beaches, respectively. We found that polyethylene types and additives are the dominant MPs in both areas. This study provided us with new insights into the human activities and natural processes in these marine environments. To solve the problem of plastic accumulation in the marine environment, the government should first play an active role in addressing the problem of plastic waste by introducing laws to control the sources of plastic waste and the use of plastic additives.

Site selection of check dams using geospatial techniques in Debre Berhan region, Ethiopia - water management perspective.

Remote sensing and GIS technology were very helpful to determine an appropriate location of freshwater storage in Amhara, Ethiopia. The techniques were used to investigate the impact of lithology, surface geomorphology, slope parameters, drainage flow, drainage density, lineament density, land cover parameters on relief, and aerial and linear features and to understand their interrelationships. Morphometric parameters such as mean stream length (Lsm), stream length ratio (RL), bifurcation ratio (Rb), mean bifurcation ratio (Rbm), relief ratio (Rh), drainage density (Dd), stream frequency (Fs), drainage texture (Rt), form factor (Rf), circularity ratio (Rc), and elongation ratio (Re) were calculated. Spatial maps of morphometric parameters were produced by using AHP (analytical hierarchy process) of ArcGIS 10.3. Final priority map was generated by the overlay of those parameters with five categories of poor (16.6%), low (41.63%), moderate (29.61%), high (8.88%), and very high (3.28%) storage locations. The map showed that this study area belonged to the low to moderate storage location. The results exhibit precision-based assessment of the suitability for the dam construction sites of 6, 7, and 9 sub-basin zones. The outcome of this study strengthens the knowledge of geospatial analysis for water resources vulnerability and also allows policymakers in this drought-prone area to sustainably manage water supplies.

Measurement of submarine groundwater discharge (SGD) into Tiruchendur coast at southeast India using 222Rn as a naturally occurring tracer.

Application of natural tracers such as radon isotope mass balance has been useful in estimating the submarine groundwater discharge (SGD). This study used 222Rn and evaluated the magnitude of SGD at Tiruchendur coast of southeast India in the Gulf of Mannar (Indian Ocean). Higher magnitudes of 222Rn in the porewater and seawater in comparison with the groundwater suggest simultaneous SGD with fluxes of 0.1-0.25 m3 m-2 d-1 at offshore and 0.4-0.20 m3 m-2 d-1 at the near shore. These baseline data would contribute to the management and protection of the Gulf of Mannar region in near future.

Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

Microplastics and trace metals in fish species of the Gulf of Mannar (Indian Ocean) and evaluation of human health.

The importance of microplastic (MPs) contamination in marine environments is reflected by increasing number of studies in fish species. Some even dedicated to the toxicological effects from the ingestion. Microplastics (MPs) and their trace metal composition were examined in the muscle and intestine of five commercially important fish species (i.e., Sufflamen fraenatus, Heniochus acuminatus, Atropus atropos, Pseudotriacanthus and Leiognathus brevirostris) from Thoothukudi at the Gulf of Mannar coast in south India. The abundance and morphology of MPs (size, shape, and texture) in muscle and intestinal were investigated by micro-Fourier Transform Infrared Spectroscopy (µ-FT-IR) and atomic force microscope (AFM). ICP-OES was used to investigate the adsorption/leaching of trace metals in microplastics in order to assess health risk for adults and children. Particles of 100-250 µm and white color dominated, and the mean abundances (items/100 g) of total MPs were more in Pseudotriacanthus (muscle: 51.2; intestine: 50.1) compared to Heniochus acuminatus (muscle: 9.6; intestine: 15), Leiognathus brevirostris (muscle: 12; intestine: 13.2) and Atropus atropus (muscle: 15.2; intestine: 44.1). Polyethylene (35.3%), polypropylene (27.2%), polyamide (nylon) (22.2%) and fiber (15.3%) represented the MPs present in muscles, and polyamide (nylon) (30.2%), polyethylene (28.1%), polypropylene (25.9%), and fiber (15.8%) composed the intestine MPs. We estimated possible consumption of 121-456 items of MPs/week by adults and about 19-68 items of MPs/week by children by considering the sizes of safe meals. Zn, Cu, Mn and Cr in these fish species reflected influence of the sewage waste. However, the non-carcinogenic risk evaluated through EDI, THQ, HI, and CR did not suggest any immediate health problem for the consumers.

Assessment of groundwater from an industrial coastal area of south India for human health risk from consumption and irrigation suitability.

We assessed the groundwater quality in an industrial area (Tiruchendur Taluk) of Tamil Nadu state in coastal south India for human health risk from drinking as well as irrigation suitability by using the drinking water quality index (DWQI), irrigation factors (sodium adsorption ratio, sodium percentage, residual sodium carbonate and permeability index) and health hazard valuation (THQI- by consuming NO3- and F-). About 57% of the samples represented Ca2+-Mg2+-Cl--SO42- facies and the anthropological unhygienic inputs elevated the salinity. Our results indicated that all the samples are unsuitable for drinking (DWQI up to 1063) and almost half of them are also unsuitable for irrigation due to sodium risk. Total hazard quotient index (THQI; HQ nitrate and HQ fluoride) suggested the order of health risk as children > women > men with about 64%, 70% and 79% of the samples posing non-carcinogenic risks for men, women and children, respectively. Different mitigation measures and sustainable development should be enforced to minimize the health issues from contamination caused by industries, fertilizers in agro-fields and natural processes and reduce the sodium dominance in groundwater. The spatial distribution maps of this study could also be helpful in organization of proper treatment plans to provide safe and hygienic groundwater to the community.

Modeling of aquifer vulnerability index using deep learning neural networks coupling with optimization algorithms.

A reliable assessment of the aquifer contamination vulnerability is essential for the conservation and management of groundwater resources. In this study, a recent technique in artificial intelligence modeling and computational optimization algorithms have been adopted to enhance the groundwater contamination vulnerability assessment. The original DRASTIC model (ODM) suffers from the inherited subjectivity and a lack of robustness to assess the final aquifer vulnerability to nitrate contamination. To overcome the drawbacks of the ODM, and to maximize the accuracy of the final contamination vulnerability index, two levels of modeling strategy were proposed. The first modeling strategy used particle swarm optimization (PSO) and differential evolution (DE) algorithms to determine the effective weights of DRASTIC parameters and to produce new indices of ODVI-PSO and ODVI-DE based on the ODM formula. For strategy-2, a deep learning neural networks (DLNN) model used two indices resulting from strategy-1 as the input data. The adjusted vulnerability index in strategy-2 using the DLNN model showed more superior performance compared to the other index models when it was validated for nitrate values. Study results affirmed the capability of the DLNN model in strategy-2 to extract the further information from ODVI-PSO and ODVI-DE indices. This research concluded that strategy-2 provided higher accuracy for modeling the aquifer contamination vulnerability in the study area and established the efficient applicability for the aquifer contamination vulnerability modeling.

ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area.

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular models. ANFIS-MOA models were also useful for resolving the subjectivity of physical and hydrogeological parameters in original DRASTIC method (ODM) and MDM. It is expected that ANFIS-PSO models will produce the excellent results in assessing groundwater contamination vulnerability and that they can greatly contribute to the groundwater security in other areas of the world as well as Miryang City of South Korea.

Processes and characteristics of hydrogeochemical variations between unconfined and confined aquifer systems: a case study of the Nakdong River Basin in Busan City, Korea.

This study is to assess the hydrogeochemical characteristics of groundwater at the deltaic region of the Nakdong River Basin in the Busan Metropolitan City of Korea. The study area is covered by the Quaternary sedimentary deposits and the Cretaceous granites associated with unconformity. The thick sedimentary deposits consists of two aquifers, i.e., unconfined and confined aquifers on the basis of clay deposit. Groundwater samples were collected from seven boreholes: two from unconfined aquifer and five from confined aquifer systems during the wet season of 2017 year. ORP and DO indicates that the groundwater of the unconfined aquifer exists in the oxidization condition and that of the confined aquifer pertains in the reduction condition. Piper's trilinear diagram shows CaSO4 type for groundwater of the unconfined aquifer, and NaCl type for that of the confined aquifer. Ionic concentrations of groundwater increase in the confined aquifer because of direct and reverse ion exchange processes. Carbonate weathering and evaporation are other mechanisms in the water-rock interaction. Saturation indices of dolomite and calcite are observed as oversaturated, while halite reveals undersaturation. Hierarchical cluster analysis (HCA) exhibits that cluster 1 and cluster 2 represents the properties of groundwater in unconfined and confined aquifers, respectively. Factor analysis shows that groundwater of the confined aquifer is much influenced by seawater, and includes heavy metals of iron and aluminum. Groundwater samples in unconfined and confined aquifers are located at the rock weathering and evaporation zones in the Gibbs diagram. Inverse geochemical modeling of PHREEQC code suggests that carbonate dissolution and ion exchange of major ions are the prevailing geochemical processes. This comprehensive research provides the distinguished hydrogeochemical characteristics of groundwater in confined and unconfined aquifer systems of the Nakdong River Basin in Busan City, Korea.

Factors controlling groundwater quality in the Yeonjegu District of Busan City, Korea, using the hydrogeochemical processes and fuzzy GIS.

The hydrogeochemical processes and fuzzy GIS techniques were used to evaluate the groundwater quality in the Yeonjegu district of Busan Metropolitan City, Korea. The highest concentrations of major ions were mainly related to the local geology. The seawater intrusion into the river water and municipal contaminants were secondary contamination sources of groundwater in the study area. Factor analysis represented the contamination sources of the mineral dissolution of the host rocks and domestic influences. The Gibbs plot exhibited that the major ions were derived from the rock weathering condition. Piper's trilinear diagram showed that the groundwater quality was classified into five types of CaHCO3, NaHCO3, NaCl, CaCl2, and CaSO4 types in that order. The ionic relationship and the saturation mineral index of the ions indicated that the evaporation, dissolution, and precipitation processes controlled the groundwater chemistry. The fuzzy GIS map showed that highly contaminated groundwater occurred in the northeastern and the central parts and that the groundwater of medium quality appeared in most parts of the study area. It suggested that the groundwater quality of the study area was influenced by local geology, seawater intrusion, and municipal contaminants. This research clearly demonstrated that the geochemical analyses and fuzzy GIS method were very useful to identify the contaminant sources and the location of good groundwater quality.