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1.
While it remains the primary source of safe drinking and irrigation water in northwest Iran's Maku Plain, the region's groundwater is prone to fluoride contamination. Accordingly, modeling techniques to accurately predict groundwater fluoride concentration are required. The current paper advances several novel data mining algorithms including Lazy learners [instance-based K-nearest neighbors (IBK); locally weighted learning (LWL); and KStar], a tree-based algorithm (M5P), and a meta classifier algorithm [regression by discretization (RBD)] to predict groundwater fluoride concentration. Drawing on several groundwater quality variables (e.g., concentrations), measured in each of 143 samples collected between 2004 and 2008, several models predicting groundwater fluoride concentrations were developed. The full dataset was divided into two subsets: 70% for model training (calibration) and 30% for model evaluation (validation). Models were validated using several statistical evaluation criteria and three visual evaluation approaches (i.e., scatter plots, Taylor and Violin diagrams). Although Na+ and Ca2+ showed the greatest positive and negative correlations with fluoride (r = 0.59 and −0.39, respectively), they were insufficient to reliably predict fluoride levels; therefore, other water quality variables, including those weakly correlated with fluoride, should be considered as inputs for fluoride prediction. The IBK model outperformed other models in fluoride contamination prediction, followed by KStar, RBD, M5P, and LWL. The RBD and M5P models were the least accurate in terms of predicting peaks in fluoride concentration values. Results of the current study can be used to support practical and sustainable management of water and groundwater resources.  相似文献   

2.
Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.  相似文献   

3.
The ability of the extreme learning machine (ELM) is investigated in modelling groundwater level (GWL) fluctuations using hydro-climatic data obtained for Hormozgan Province, southern Iran. Monthly precipitation, evaporation and previous GWL data were used as model inputs. Developed ELM models were compared with the artificial neural networks (ANN) and radial basis function (RBF) models. The models were also compared with the autoregressive moving average (ARMA), and evaluated using mean square errors, mean absolute error, Nash-Sutcliffe efficiency and determination coefficient statistics. All the data-driven models had better accuracy than the ARMA, and the ELM model’s performance was superior to that of the ANN and RBF models in modelling 1-, 2- and 3-month-ahead GWL. The RMSE accuracy of the ANN model was increased by 37, 34 and 52% using ELM for the 1-, 2- and 3-month-ahead forecasts, respectively. The accuracy of the ELM models was found to be less sensitive to increasing lead time.  相似文献   

4.
ABSTRACT

Infiltration plays a fundamental role in streamflow, groundwater recharge, subsurface flow, and surface and subsurface water quality and quantity. In this study, adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and random forest (RF) models were used to determine cumulative infiltration and infiltration rate in arid areas in Iran. The input data were sand, clay, silt, density of soil and soil moisture, while the output data were cumulative infiltration and infiltration rate, the latter measured using a double-ring infiltrometer at 16 locations. The results show that SVM with radial basis kernel function better estimated cumulative infiltration (RMSE = 0.2791 cm) compared to the other models. Also, SVM with M4 radial basis kernel function better estimated the infiltration rate (RMSE = 0.0633 cm/h) than the ANFIS and RF models. Thus, SVM was found to be the most suitable model for modelling infiltration in the study area.  相似文献   

5.
Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze \( {\text{NO}}_{ 3}^{ - } \) concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the \( {\text{NO}}_{3}^{ - } \) data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using \( {\text{NO}}_{3}^{ - } \) concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and \( {\text{NO}}_{3}^{ - } \) concentration values. After model training, the AI models are verified by the second \( {\text{NO}}_{3}^{ - } \) concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.  相似文献   

6.
The use of electrical conductivity (EC) as a water quality indicator is useful for estimating the mineralization and salinity of water. The objectives of this study were to explore, for the first time, extreme learning machine (ELM) and wavelet-extreme learning machine hybrid (WA-ELM) models to forecast multi-step-ahead EC and to employ an integrated method to combine the advantages of WA-ELM models, which utilized the boosting ensemble method. For comparative purposes, an adaptive neuro-fuzzy inference system (ANFIS) model, and a WA-ANFIS model, were also developed. The study area was the Aji-Chay River at the Akhula hydrometric station in Northwestern Iran. A total of 315 monthly EC (µS/cm) datasets (1984–2011) were used, in which the first 284 datasets (90% of total datasets) were considered for training and the remaining 31 (10% of total datasets) were used for model testing. Autocorrelation function (ACF) and partial autocorrelation function (PACF) demonstrated that the 6-month lags were potential input time lags. The results illustrated that the single ELM and ANFIS models were unable to forecast the multi-step-ahead EC in terms of root mean square error (RMSE), coefficient of determination (R2) and Nash–Sutcliffe model efficiency coefficient (NSC). To develop the hybrid WA-ELM and WA-ANFIS models, the original time series of lags as inputs, and time series of 1, 2 and 3 month-step-ahead EC values as outputs, were decomposed into several sub-time series using different maximal overlap discrete wavelet transform (MODWT) functions, namely Daubechies, Symlet, Haar and Coiflet of different orders at level three. These sub-time series were then used in the ELM and ANFIS models as an input dataset to forecast the multi-step-ahead EC. The results indicated that single WA-ELM and WA-ANFIS models performed better than any ELM and ANFIS models. Also, WA-ELM models outperformed WA-ANFIS models. To develop the boosting multi-WA-ELM and multi-WA-ANFIS ensemble models, a least squares boosting (LSBoost) algorithm was used. The results showed that boosting multi-WA-ELM and multi-WA-ANFIS ensemble models outperformed the individual WA-ELM and WA-ANFIS models.  相似文献   

7.
《水文科学杂志》2012,57(15):1857-1866
ABSTRACT

Daily streamflow forecasting is a challenging and essential task for water resource management. The main goal of this study was to compare the accuracy of five data-driven models: extreme learning machine (basic ELM), extreme learning machine with kernels (ELM-kernel), random forest (RF), back-propagation neural network (BPNN) and support vector machine (SVR). The results show that the ELM-kernel model provided a superior alternative to the other models, and the basic ELM model had the poorest performance. To further evaluate the predictive capacities of the five models, the estimations of low flow and high flow in the testing dataset were compared. The RF model was slightly superior to the other models in predicting the peak flows, and the ELM-kernel model showed the highest prediction precision of low flows. There was no single model that showed obvious advantages over the other models in this study. Therefore, further exploration is required for the hydrological forecasting problems.  相似文献   

8.
Nitrate transport in the unsaturated zone of a riverbank filtration (RBF) system in Karany, Czech Republic, was studied. Previous study of the system estimated RBF recharge as 60% riverbank filtrate and 40% local groundwater contaminated by nitrates. Nitrate concentrations observed in RBF recently cannot be explained by simple groundwater contamination and a new conception of groundwater recharge is suggested. A two‐component model based on water 18O data modelled recharge of local groundwater. One component of groundwater recharge is rainfall and irrigation water moving through the unsaturated zone of the Quaternary sediments in piston flow. The second component is groundwater from the Cretaceous deposits with a free water table. Both the components of groundwater recharge have different nitrate concentrations, and resulting contamination of groundwater depends on the participation of water from Quaternary and Cretaceous deposits. Nitrates' origins and their mixing in the subsurface were traced by 15N data. Nitrate transport from the unsaturated zone is important and time variable source of groundwater contamination. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

9.
ABSTRACT

The groundwater contamination risk in future climates was investigated at three locations in Sweden. Solute transport penetration depths were simulated using the HYDRUS-1D model using historical data and an ensemble of climate projections including two global climate models (GCMs), three emission scenarios and one regional climate model. Most projections indicated increasing precipitation and evapotranspiration until mid-century with a further increase at end-century. Results showed both increasing and decreasing groundwater contamination risks depending on emission scenario and GCM. Generally, the groundwater contamination risk is likely to be unchanged until mid-century, but higher at the end of the century. Soil and site specific relationships between Δ(P – PET) (i.e. change in the difference between precipitation, P, and potential evapotranspiration, PET) and changes in solute transport depths were determined. Using this, changes in solute transport depths for other climate projections can be assessed.  相似文献   

10.
ABSTRACT

The potential of different models – deep echo state network (DeepESN), extreme learning machine (ELM), extra tree (ET), and regression tree (RT) – in estimating dew point temperature by using meteorological variables is investigated. The variables consist of daily records of average air temperature, atmospheric pressure, relative humidity, wind speed, solar radiation, and dew point temperature (Tdew) from Seoul and Incheon stations, Republic of Korea. Evaluation of the model performance shows that the models with five and three-input variables yielded better accuracy than the other models in these two stations, respectively. In terms of root-mean-square error, there was significant increase in accuracy when using the DeepESN model compared to the ELM (18%), ET (58%), and RT (64%) models at Seoul station and the ELM (12%), ET (23%), and RT (49%) models at Incheon. The results show that the proposed DeepESN model performed better than the other models in forecasting Tdew values.  相似文献   

11.
The aim of this study is to identify, in a small catchment area located within a tropical forest, the pedological compartments in which the export of nutrients and chemical erosion of solutes occur during a stormflow event. The catchment area displays two types of lateral flow: (i) overland flow at the surface of the soil in the litter and root mat and (ii) groundwater flow in a macroporous subsurface horizon. We interpret the variations of stream‐water chemistry during a storm‐flow event using the separation of storm‐flow hydrograph data between overland and groundwater flow, and (Cl?) as a chemical parameter characterizing the residence time of water in the soil. It appears that K+ especially was released into the throughfall, whereas Ca++, Mg++ and Na+ were clearly released from the litter. K+ disappeared rapidly from soil solution, whereas Ca++ and Mg++ were more progressively absorbed by the vegetation. The Ca++ and Mg++ contents in groundwater increased with increasing residence time owing to the transpiration of trees. The export of H4SiO4 in the overland flow was moderate, i.e. 24% of total H4SiO4 export in the stream flow, as overland flow represented 39% of total runoff. The subsurface horizon—where active groundwater flow occurs—was successively affected by chemical erosion during the storm‐flow peak, and then by neoformation of kaolinite favoured by increasing water residence time. Copyright © 2003 John Wiley & Sons, Ltd.  相似文献   

12.
We used hydrochemistry and environmental isotope data (δ18O, δD, tritium, and 14C) to investigate the characteristics of river water, groundwater, and groundwater recharge in China's Heihe River basin. The river water and groundwater could be characterized as Ca2+? Mg2+? HCO3?? SO42? and Na+? Mg2+? SO42?? Cl? types, respectively. Hydrogeochemical modelling using PHREEQC software revealed that the main hydrogeochemical processes are dissolution (except for gypsum and anhydrite) along groundwater flow paths from the upper to middle Heihe reaches. Towards the lower reaches, dolomite and calcite tend to precipitate. The isotopic data for most of the river water and groundwater lie on the global meteoric water line (GMWL) or between the GMWL and the meteoric water line in northwestern China, indicating weak evaporation. No direct relationship existed between recharge and discharge of groundwater in the middle and lower reaches based on the isotope ratios, d‐excess, and 14C values. On the basis of tritium in precipitation and by adopting an exponential piston‐flow model, we evaluated the mean residence time of shallow groundwater with high tritium activities, which was around 50 years (a). Furthermore, based on the several popular models, it is calculated that the deep groundwaters in piedmont alluvial fan zone of the middle reaches and in southern part of the lower reaches are modern water, whereas the deep groundwaters in the edge of the middle reaches and around Juyan Lake in the lower reaches of Heihe river basin are old water. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

13.
Few studies have investigated large reaches of rivers in which multiple sources of groundwater are responsible for maintaining baseflow. This paper builds upon previous work undertaken along the Fitzroy River, one of the largest perennial river systems in north‐western Australia. Synoptic regional‐scale sampling of both river water and groundwater for a suite of environmental tracers (4He, 87Sr/86Sr, 222Rn and major ions), and subsequent modeling of tracer behavior in the river, has enabled definition and quantification of groundwater input from at least three different sources. We show unambiguous evidence of both shallow “local” groundwater, possibly recharged to alluvial aquifers beneath the adjacent floodplain during recent high‐flow events, and old “regional” groundwater introduced via artesian flow from deep confined aquifers. We also invoke hyporheic exchange and either bank return flow or parafluvial flow to account for background 222Rn activities and anomalous chloride trends along river reaches where there is no evidence of the local or regional groundwater inputs. Vertical conductivity sections acquired through an airborne electromagnetic (AEM) survey provide insights to the architecture of the aquifers associated with these sources and general groundwater quality characteristics. These data indicate fresh groundwater from about 300 m below ground preferentially discharging to the river, at locations consistent with those inferred from tracer data. The results demonstrate how sampling rivers for multiple environmental tracers of different types—including stable and radioactive isotopes, dissolved gases and major ions—can significantly improve conceptualization of groundwater—surface water interaction processes, particularly when coupled with geophysical techniques in complex hydrogeological settings.  相似文献   

14.
A geochemical study was carried out in a small spa area (Onyang Spa, Korea) where intensive pumping of deep thermal groundwater (1 300 000 m3 year−1) is taking place. This has caused the deep fractures to lose their artesian pressure and the upper shallow fractures have been encroached by shallow, cold waters. To quantify the influence of long‐term heavy pumping on the quality of the geothermal water, groundwater sampling and chemical analysis, water‐level measurement, and well loggings were performed for the selected deep thermal wells and shallow cold wells. Chemical analysis results indicate a big contrast in water chemistry and origins between the two water types. Shallow groundwater shows a wider concentration ranges in solutes that are closely related to human activity, illustrating the water's vulnerability to contamination near the land surface. Plots of water chemistry as a function of fluoride reveal that the quality of the thermal water was greatly influenced by the shallow, cold groundwater and that intensive pumping of the deep thermal groundwater has caused the introduction of shallow groundwater into the deeper fractures. Although the deep and the shallow fractures were piezometrically separated to some extent, a mixing model based on fluoride and nitrate indicated that the cold‐water fractions in the thermal wells are up to 50%. This suggests that the thermal water is faced with water quality degradation by the downward flow of the shallow, cold water. Restriction on the total of all the pumpage permits per unit area is suggested to restore the artesian pressure of the deep thermal aquifer and to prevent cold‐water intrusion in the study area. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

15.
N. Subba Rao 《水文研究》2012,26(22):3344-3350
A pollution index of groundwater (PIG) is proposed for quantification of water contamination. PIG quantifies the status of concentrations of water quality measures with respect to their drinking water quality standards. The validity of the proposed index is verified by choosing the data of groundwater quality of the Varaha River Basin (Visakhapatnam District, Andhra Pradesh, India) as a case study. The computed index from the study area varies from 0.83 to 2.55. The index disseminates the area into zones of insignificant (PIG <1.0), low (PIG: 1.0 to 1.5), moderate (PIG: 1.5 to 2.0), high (PIG 2.0 to 2.5) and very high (PIG >2.5) pollution. Insignificant pollution zone is observed from the upstream area, where the groundwater is dominated by , and very high pollution zone from the downstream area, where the groundwater is associated with Cl?. This indicates that the quality of groundwater in the study area is mainly influenced by the source of geogenic origin, but it is subsequently modified by the effects of anthropogenic and marine sources. Geochemical ratios (Na+ : Cl?, : Cl?, Na+ : Ca2+ and Mg2+ : Ca2+) also form the quantitative basis of the index. The present study paves the way to implement appropriate management strategies at a specific site to circumvent the pollution. As the classification of the pollution zones with PIG depends upon the drinking water quality standards, it becomes a universal assessment tool for groundwater contamination at any test area. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

16.
Xiaohu Wen  Meina Diao  De Wang  Meng Gao 《水文研究》2012,26(15):2322-2332
Groundwater salinization has become a crucial environmental problem worldwide and is considered the most widespread form of groundwater contamination in the coastal zone. In this study, a hydrochemical investigation was conducted in the eastern coastal shallow aquifer of Laizhou Bay to identify the hydrochemical characteristics and the salinity of groundwater using ionic ratios, deficit or excess of each ions, saturation indices and factor analysis. The results indicate that groundwater in the study area showed wide ranges and high standard deviations for most of hydrochemical parameters and can be classified into two hydrochemical facies, Ca2+‐Mg2+‐Cl‐ facies and Na+‐Cl‐ facies. The ionic ratio, deficit or excess of each ions and SI were applied to evaluate hydrochemical processes. The results obtained indicate that the salinization processes in the coastal zones were inverse cation exchange, dissolution of calcite and dolomite, and intensive agricultural practices. Factor analysis shows that three factors were determined (Factor 1: TDS, EC, Cl‐, Mg2+, Na+, K+, Ca2+ and SO42‐; Factor 2: HCO3‐ and pH; Factor 3: NO3‐ and pH), representing the signature of seawater intrusion in the coastal zone, weathering of water–soil/rock interaction, and nitrate contamination, respectively. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

17.
Environmental dating tracers (3H, 3He, 4He, CFC-12, CFC-11, and SF6) and the natural spring response (hydrochemistry, water temperature, and hydrodynamics) were jointly used to assess mixing processes and to characterize groundwater flow in a relatively small carbonate aquifer with complex geology in southern Spain. Results evidence a marked karst behaviour of some temporary outlets, with sharp and rapid responses to precipitation events, while some perennial springs show buffer and delayed variations with respect to recharge periods. The general geochemical evolution shows a pattern, from higher to lower altitudes, in which mineralization and the Mg/Ca ratio rise, evidencing longer water–rock interaction. The large SF6 concentrations in groundwater suggest terrigenic production, whereas CFC-11 values are affected by sorption or degradation. The groundwater age in the perennial springs—as deduced from CFC-12 and 3H/3He—points to mean residence times of several decades, although the large amount of radiogenic 4He in samples indicate a contribution of old groundwater (free of 3H and CFC-12). Lumped parameter models and shape-free models were created based on 3H, tritiogenic 3He, CFC-12, and radiogenic 4He data in order to interpret the age distribution of the samples. Results evidence the existence of two mixing components, with an old fraction ranging between 160 and 220 years in age. The correlation of physicochemical parameters with some dating parameters, derived from the mixing models, serves to explain the hydrogeochemical processes occurring within the system. Altogether, long residence times are shown to be possible in small alpine systems with a clearly karst behaviour if the geological setting features highly tectonized media including units with diverse hydrogeological characteristics. These findings highlight the importance of applying different approaches, including groundwater dating techniques, when studying such groundwater flow regimes.  相似文献   

18.
The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R2 = 0.25–0.90) was slightly worse than that of the RF algorithm (R2 = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R2 = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R2 = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.  相似文献   

19.
Abstract

Reports on the occurrence of fluoride in natural water resources and the associated health hazards due to human consumption have been made from many parts of India during the last decade. With the objective of organizing a systematic scientific programme to understand the behaviour of fluoride in natural water resources in relation to the local hydrogeological and climatic conditions and agricultural use, a typical area constituting the lower Vamsadhara River basin was chosen for a detailed study. High fluoride concentrations in the groundwater reaching a maximum of 3.4 mg 1?1 were observed to be associated with weathered formations of pyroxene amphibolites and pegmatites. The groundwater in the clayey soils contained much less fluoride as compared to the sandy soils. The complex depositional pattern of these sandy and clayey soils plays an important role in the uneven spatial distribution of fluoride in the groundwater. The contribution of fluoride from geological formations is far greater than that from agriculture: the maximum yield of fluoride by superphosphate fertilizer to irrigation water is observed to be 0.34 mg 1?1. The fluoride concentration is expected to be increased in the future as the groundwater is subsaturated with respect to fluorite. An inverse relationship between F and Ca and positive relationships of F with Na, HCO3, PO4 and electrical conductivity were observed. Best relationships were obtained in the fluoride range of 1.0–3.4 mg 1?1.  相似文献   

20.
Multivariate statistical techniques, cluster and factor analyses were applied on the Amman/Wadi Sir groundwater chemistry, Yarmouk River basin, north Jordan. The main objective was to investigate the main processes affecting the groundwater chemical quality and its evolution. The k‐means cluster analysis yields three groups with distinct ionic concentrations. Cluster 1 comprises the vast majority of the sampled wells, and the water that belongs to this cluster can be classified as freshwater. Cluster 2 comprises only 2% of the sampled wells; it has the highest ionic concentration. The water of this cluster can be classified as brackish water. Cluster 3 involves 23% of the sampled wells, and it has total ionic concentration intermediate to that of clusters 1 and 2. Factor analysis yields a three‐factor model, which explains 76.77% of the groundwater quality variation. Factor 1 ‘salinity factor’ involves EC, Na+, Cl‐, SO4‐2, K+ and Mg+2 and reflects groundwater salinization because of overpumping. Factor 2 ‘hardness factor’ includes Ca+2, HCO3‐ and the pH value and signifies soil–water/rock interaction. Factor 3 ‘nitrate factor’ involves only NO3‐ and points to groundwater contamination because of human activities, mainly untreated wastewater, and crops and animal cultivation in the unconfined portion of the aquifer. Factors 1 and 3 can be described as human‐induced factors, whereas factor 2 can be described as geogenic factor. Factors' scores were mapped to deduce the controlling processes on the groundwater chemistry. Stable isotope composition of 18O and 2H has revealed that the groundwater is a mixture of two water types. The radioactive isotopes tritium and 14 C were used to evaluate present day recharge to the aquifer and to estimate the groundwater age, respectively. Present day recharge to the groundwater is taking place in the unconfined portion of the aquifer as it is indicated by the measurable tritium content and low groundwater age. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

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