فهرست مطالب اصغر اصغری مقدم

Recently, due to the trend of decreasing rainfall and increasing groundwater pumping rate, there have been concerns about the risks caused by the decrease in the volume of aquifer reserves and the drop in the groundwater level, and as a consequence the land subsidence. Also, in Ajabshir plain due to a gradual decrease in the water level, it is necessary to estimate the subsidence and investigate the subsidence potential to prevent its harmful risks in the future. For this purpose, using the ALPRIFT framework, which includes seven layers of parameters affecting subsidence, the subsidence potential map was zoned. The subsidence potential index was obtained in low and moderate ranges. In the next step, using Sentinel-1 satellite images, the subsidence during the years 2020-2021 was estimated to be 2.4 cm, which had a significant correlation with the groundwater level of the water year 2020-2021 and subsidence potential. In addition, artificial intelligence optimization methods including fuzzy logic (Sugeno) and genetic algorithm were used in order to fix the defects of applying expert opinions and increase the correlation between subsidence (Insar) and ALPRIFT, among these models, Sugeno's fuzzy method provided the best correlation between the two subsidence maps and ALPRIFT. The correlation between subsidence with ALPRIFT, ALPRIFT-GA and ALPRIFT-SFL was obtained as 0.46, 0.62 and 0.72 respectively.

The drinking water of Bostanabad region is mostly supplied from the groundwater resources of Bostanabad Plain. The aim of this study is to select the most reasonable and suitable method for investigating the quality of this resources. For this purpose, the rate and weight of the water quality index were calculated using multi-criteria decision making methods. The quality of groundwater resources was investigated by combining entropy and AHP weighting methods with TOPSIS and WQI rating methods. Parameters of pH, TDS, Na, SO4, Cl, NO3 and F were used in the combined methods of Entropy-WQI, AHP-WQI, Entropy-TOPSIS and AHP-TOPSIS. The coefficient of determination (R2) between the average rank of the samples and the ranks obtained from each of the methods showed that the AHP-TOPSIS method has a high ability to water ranking and classification. The samples were located in the Entropy-WQI method in 2 classes of good and average, AHP-WQI method in 3 classes of very good, good, and average, Entropy-TOPSIS method in 5 classes of very good, good, average, poor, and very poor and AHP-TOPSIS method in 4 classes very good, good, medium and poor. By comparing the combined methods, it became clear that AHP was better than entropy in terms of weighting and TOPSIS was better than the classical WQI method in terms of rating. The results showed that the entropy method is caught in an error regarding the values that are similar and close to the minimum value of the parameters that have a direct relationship with entropy.

Marand plain is one of the fertile plains in the province of East Azarbaijan which its aquifer provides drinking, agricultural, industrial water demands of the area. Therefore, the evaluation and protection of the quantity and quality of these water resources is extremely The purpose of this research is the qualitative zoning of Marand plain groundwater in terms of drinking، agriculture and industry. For this purpose, 91 samples were taken from operational wells in June 2019. Hydrogeochemical characteristics and concentration of major and minor ions and some heavy metals were measured by means of standard methods. The usefulness of the Plain groundwater for various consumptions was investigated using graphical methods and quality criteria of Schuler, Wilcox, and Langelier classification. In this regard, the final maps of quality zoning were prepared after creating the raster layers of the effective parameters, classification, and integration using GIS and the geostatistical interpolation method. The industrial, agricultural and drinking water quality were evaluated by using langelier scaling, Wilcox and Schuler methosd, respectively. Analyzing and Interpreting of zoning maps with geological information, land use, distribution of industrial units was carried out in the plain and the results show that the quality of groundwater is inappropriate mainly in the northern, central, western parts of the plain due to the precens of salty and gypsum formations in northen and western part of the plain.

Salmas plain aquifer is one of the aquifers in West Azerbaijan province, which is exposing to contamination risk of various contaminants from geogenic origin such as arsenic. This is due to hot springs and geological conditions of the area. Therefore, it is necessary to assess the contamination risk to arsenic contaminant and identification of high-risk areas in this aquifer. In this study, the contamination risk of Salmas aquifer was investigated using OSPRC method. In OSPRC method, the contamination risk investigated by considering the origin, source, pathways, receptor and consequence. In this method, the source of contamination was identified then specific vulnerability to arsenic contaminants as pathway was investigated using GMDH neural network model and six parameters of SPECTR method. The parameters of SPECTR method are slope, pH, electrical conductivity, hydraulic conductivity, water table and recharge. The testing RMSE and r values of the GMDH neural network model were 0.036 and 0.902, respectively. The contamination risk map was obtained by multiplying the specific vulnerability in the groundwater velocity. This map showed that the risk of contamination to arsenic contaminant is higher in the western and southwestern parts of the aquifer than the other parts of the aquifer. In general, the contamination risk of Salmas aquifer to the arsenic contaminants is very low.

Marand aquifer is one of the large plains with extensive agricultural and industrial activities and it is categorized in critical aquifer class. One of the problems of groundwater decline is groundwater quality changes that affect how they are used and managed. Therefore, in this study, using 46 water samples from wells, qanats and springs in June 2014 and June 2017 and analyzing the parameters of main, secondary and rare ions to calculate the GQI and GWQI groundwater quality indices. According to the output maps of these two indicators, which show the difference in determining the quality of drinking water in the region, it was not possible to choose one of these two indicators to determine the drinking water capacity of the region. The two indicators used, in order to achieve a single map, the method of unsupervised combination was introduced and used. The results indicate that a higher CI than the results of the two indicators. The results showed that Marand plain is in the middle and acceptable classes in terms of GWQI and GQI index. The highest and lowest GWQI and GQI indices were observed in the south and northwest of the plain, respectively. Comparison of drinking water quality index maps of the study area in 2014 and 2017 shows that in 2017 the area of plain, indicates a decrease in water quality over the last 5 years.

Shortage of surface water resources and excessive exploitation of groundwater resources in Bostanabad plain has caused a sharp decline in groundwater level and thus reduced its quality. The purpose of this study is to investigate the concentrations of major, minor and trace elements in groundwater resources with statistical methods. Therefore, 33 samples were collected for chemical analysis of major and minor ions and metalloid and heavy metals from spring and wells in the region. Physicochemical parameters of the samples were investigated using cluster and discriminant analysis and Pearson correlation coefficient. Using cluster analysis it was found that the samples are located in two clusters; cluster 1 belongs to the samples collected from East and Northeast of the area and is affected by Miocene evaporitic formations of the region, while cluster 2 is mainly related to the samples which is impacted from alluvial tuffs of the Sahand volcanic Mountain. Na, Mg, NO3, PO4 and SiO2 parameters were determined in 5 steps as the most appropriate parameter to predict clustering. Increasing salinity will be effective in increasing the mobility and release of manganese. The correlation between calcium, arsenic and iron showed that the sorption of arsenic to the surface of iron hydroxide increases in the presence of calcium. The relation between iron and manganese will be strong due to the common sensitivity to changes in oxidation-reduction potential, similar geochemical properties as well as the simultaneous presence of iron and manganese (hydro) oxides in the upper layer of soil.

Nitrate is one of the most common contaminants that originates from fertilizers, pesticides or domestic and industrial wastewater. This non-point source contaminant exposes the Salmas plain aquifer to groundwater pollution due to extensive agricultural activity. Therefore, it is necessary to assess the contamination risk of aquifer to high nitrate concentration and identify the high-risk areas in this aquifer. In this research, the contamination risk of Salmas plain aquifer was investigated using DRASTIC-L framework and groundwater velocity. For this purpose, after constructing of DRASTIC-L framework, the weights of eight layers of the framework optimized by genetic algorithm to obtain the aquifer vulnerability map to nitrate contaminant. Finally, the contamination risk map to nitrate was achieved from multiplying the aquifer vulnerability and groundwater velocity. The results showed that against to the eastern and central parts of the aquifer, the contamination risk of the aquifer is high in the western part of the aquifer.

In recent decades, increasing of population and development of technology and, consequently, intense agricultural and industrial activities have exposed groundwater resources to a variety of pollutants. Ajabshir plain, located in the southwest of East Azarbaijan province and southeast of Urmia Lake, is one of the areas that has faced groundwater contamination and needs more qualitative studies. In this study, first, the conventional drastic method was used to investigate the potential of nitrate contamination in Ajabshir plain, and then the optimization process was performed using the methods of genetic algorithm and fuzzy logic (Sugeno). The index values in the conventional drastic method were obtained from 87 to 145, as well as the drastic index values obtained by considering the weights of the genetic algorithm and optimization with fuzzy logic, from 47 to 74 and 0.01 to 0.6, respectively. According to the allergy classification the ordinary drastic is located in low, low to medium, and medium to high ranges, in which areas from the north of the plain and the north of Ajabshir city had moderate to high vulnerability index. Also, the optimized variables with genetic algorithm and fuzzy logic are in the safe zone in terms of contamination potential due to lower index values than 79. The normal drastic correlation coefficient, genetic algorithm, and fuzzy logic method with nitrate concentration were 0.2,73, 0.57, and 0.796, respectively. Therefore, the results show the superiority of the fuzzy logic method over other methods.

In recent years, the water level of Lake Urmia has been increasingly reduced and a huge amount of sediment and salt has remained in its bed. Due to the geology around the lake as well as the effluents and sewage that have entered the lake, harmful elements have entered the lake water. With the regression of the lake, the complex contaminants in the water have been transferred to the sediments and salts of the lake bed, which in case of possible conversion of these sediments and salts containing hazardous elements into fine dust, will affect the surrounding areas. In this study, the content of heavy harmful metals (As, Cr, Ni, Cu, Pb, Zn, Co, Mn, V, Cd and Hg) and radioactive materials (Th and U) in sediments and salts of Lake Urmia were evaluated. . The purpose of this study is to identify and study the source of contamination of harmful elements in sediments and residual salts in the dried bed of the semi-eastern part and between the passage of Lake Urmia.

To investigate, sampling of sediments and surface salts of the lake was done from the eastern and middle part of the lake and the samples were geochemically evaluated and statistically analyzed.

Comparison of the results of the obtained concentrations with the average crust showed that the elements As, Cu, Ni, Co, Hg, Cd, Th and U in the region are more than the average crust.

The studied elements were examined by statistical evaluations such as correlation coefficient, cluster analysis and factor analysis. The results of all three methods were consistent with each other. According to these results, the origin of U and Hg elements is geology and the origin of other human elements. The main sources of human pollution are from urban and industrial effluents and sewage in the areas around the lake.

Naqadeh plain located in the southwestern part of Urmia Lake has suitable water resources. In recent years, agricultural development and increasing industrial units, in addition to inadequate disposal of urban, industrial and agricultural wastewater to the Gedar River, increase the risk of groundwater contamination. In order to monitor the groundwater resources of this plain, 33 water samples from exploitation wells were collected during July 2016. Hydrochemical parameters and the concentration of the major, minor and heavy metals elements of collected samples were analyzed. In order to identify the origin of heavy metals and the related geogenic and anthropogenic pollution sources of them, hydrochemical diagrams, statistical analysis, spatial distribution maps and geological interpretations were used. The results indicate the concentration of some parameters including EC, and heavy metals include, Fe, Mn, and Al are higher than the international standard limits. HPI was used to understand the drinking quality of groundwater resources in regard to the concentrations of six heavy metals. Classification results show a good quality for 70% and inadequate quality for 30% of the samples. The total HPI index of Naqadeh plain is 23.24, which is lower than its critical values(100). Also, the highest HPI of sampling points with values of 161,220 and 871 threaten human health. High concentrations of heavy metals can be related to the dissolution of geological formations, mining of iron ore, and the activity of industrial units and the condensation of elements in groundwater due to high evaporation in areas with a low depth of groundwater.

Increasing population and rising water requirements have raised the use of freshwater resources such as groundwater. Therefore, assessing the vulnerability of groundwater is a suitable method for identifying the vulnerable areas and protecting these resources. Shabestar plain in East Azarbaijan province is an active agricultural area and the use of groundwater resources in this plain is important due to the shortage of annual precipitation. In this study, the DRASTIC frame work was used to assess the vulnerability of the Shabestar plain aquifer. The amount of DRASTIC vulnerability index in the study area was calculated as 53.3to 118.3. Given that the weights of the DRASTIC frame work were somewhat expert, so the main purpose of this study was improvement of the DRASTIC by two methods of Neural Network and Neuro-Fuzzy. DRASTIC inputs were introduced as inputs of the both artificial intelligence models. The corrected DRASTIC index with nitrate concentration was considered as the outputs of the models. Nitrate values were categorized into two groups of train and test. After training the model the results of the model were evaluated at the test step with nitrate concentration. The results showed that the both artificial intelligence models had the high ability to improve the DRASTIC model. Nevertheless, the neuro-fuzzy model having a higher correlation coefficient with nitrate was a suitable method for assessing the vulnerability of Shabestar plain aquifer.

In recent decades, due to growth of population and qancequently increasing demand for drinking, agriculture and industry purposes has led to consider the groundwater as the most important resource of water in the area. Therefore, it is necessary to pay attention to the quality of the groundwater in the area, along with its quantity. The objectives of this study are to investigate the possible origin of some heavy metals in the groundwater of Mashgin-Shahr plain using multivariate statistical methods including cluster analysis and factor analysis along with correlation coefficient as well as identification of factors affecting groundwater quality in the area. For this purpose, 25 groundwater samples were collected in October 2016, and measured with respect to pH, electrical conductivity, major (calcium, magnesium, sodium, potassium, chloride, sulfate, carbonate, and bicarbonate) and minor (nitrate, fluoride and silica) ions and some heavy metals/metalloid such as iron, manganese, aluminum, zinc, chromium, copper, cadmium, lead and arsenic. The analyzes show that processes such as weathering and dissolution of evaporatic and silicate formations, ion exchange and agricultural activities are effective on the groundwater quality of the area. The results of multivariate analysis show that most of the heavy are originated from volcanic formations in the area and salinity and acidity play an important role in releasing them into the groundwater. Factor analysis indicates that geogenic processes with a total of 79.9 % of variance and anthropogenic factors with a total of 6.6 % of variance control the groundwater chemistry.

A study is undertaken to exploit the contrast between disjunctive use and conjunctive use of multiple resources comprising: surface water, groundwater and wastewater. The primary aim is the increase in system reliability but decrease in water deficits and operational costs. Groundwater budget is studied by two alternatives models of (GMS) and conjunctive use of water resources (PSO) are considered for Hashtgerd basin in Iran. In both models, the objective function is minimization of mean groundwater drawdown. The first step in this approach is estimation of the hydrodynamic parameters and groundwater budget in steady and unsteady conditions using a simulation by GMS model. The next step involves incorporating these amount of groundwater budget into the optimization model. The results of the simulation model indicate that groundwater budget of Hashtgerd plain is negative, which reservoir storage loss is almost 17 MCM per year (2011-12). The results of the optimization model indicate that the most increases of the aquifer storge are in November, April and Februry, respectively, and the discharg of groundwaters in all zones of basin is located at the borderline for sixty percent of months. According to conditions in the plain and the results of the optimization model, the groundwater shoudn,t been withdrawal more than presented values when the reservoir storge changes is zero, then the maximum use of surface water and wastewater Could avoid more groundwater declination. Eventually the best solution to mange water resources to avoid over abstraction is the hybrid use of all water resources within each zone.

Lack of the proper conjunctive use of surface and groundwater resources causes large water stresses in one of these resources. Conjunctive use of surface and groundwater, especially in arid and semi-arid regions, is a scientific and practical solution for sustainable water resources management. The aim of this research was to prepare some mathematical modeling to apply the conjunctive use of surface and groundwater in the Dehloran plain aquifer. In this study, the mathematical model of the Dehloran plain aquifer was developed using GMS 9.1 and the river data were entered. For the steady state condition, the time series data in the average year 2010-2011 were utilized. In the next step, the time series data from October, 2010, to September, 2011, were used for the unsteady state analysis. In the unsteady state, four stress periods were taken; then the model calibration was carried out in three steps for each stress period; after the optimization of the hydrogeological parameters of the model, its verification was done for the period of 2011-2012 period. After the calibration of the model in the unsteady state, the values of the mean error (ME), the mean absolute error (MAE) and the root mean squared (RMS) errors measured in piezometers were obtained to be -0.24, 0.46 and 0.65, respectively. The results of verification confirmed the ability of the model in simulating the natural conditions of the aquifer. Finally, applying different scenarios to the model showed that the proper conjunctive use of surface and groundwater could increase the volume of water at a rate of 2.23 million cubic meters per year.

In the recent decades, due to the complexity and nonlinearity of aquifers, artificial intelligence (AI) models have been extensively used in aquifer modeling. The purpose of this research is GWL predicting using hybrid of Self Organizing Map (SOM)-clustering method with Artificial Intelligence (AI) approaches, including support vector machine (SVM) and fuzzy logic (FL). The basic concept and theory of SVM was introduced by Vapnik (1995). The SVM implement the structural risk minimization (SRM) principle. The most important concept of SVM is minimizing an upper bound to the generalization error, instead of minimizing the training error. SVM has two outstanding of excellent generalization capability, and sparse representation. Zadeh (1965) introduced the fuzzy sets. A fuzzy system includes three parts: 1. Fuzzification, the degree of membership in a fuzzy set is defined through a membership function; 2. Fuzzy rule, A fuzzy rule-based model operates on an if-then principle; 3. Defuzzification. Basic FL models and clustering techniques have been combined to provide objective FL modelling techniques, but there are variations with respect to the type of their output membership function and the implication methods. As an efficient mathematical tool, SOM may be used to visualize a high-dimensional data set (Nourani et al, 2016). SOMs reduce data dimensions and show similar patterns. Each SOM network typically includes one input and Kohonen layer. This method may reduce the aquifer's heterogeneity due to unsupervised classification of the aquifers. Based on availability of data, 10 observation wells (OW1-OW10) in the study area were selected for predicting groundwater level. The monthly groundwater level at previous time (GWL_(t_0-1)), Monthly temperature at current time (T_(t_0 )), Monthly precipitation at current time (P_(t_0 )) and monthly discharge of the Zola river at current time (Q_(t_0 )) were used as inputs of models. All data were monthly, of 15 years (2001-2016). 80% of data were used for training and 20% for test step. In order to examine the effectiveness of the models in predicting GWL, the performance measure was quantified for all models using three indices: Root Mean Squared Error (RMSE), Coefficient of determination (r2) and Nash-Sutcliffe Efficiency (NSE). The Salmas plain with an area about 550 km2, located at north of West Azarbaijan province, northwest of Iran. This area is a part of the Urmia Lake catchment and considered as a semi-arid and cold zone, with average annual temperature of 10.36 °C. Of the 27 observation wells (OW) in the aquifer of Salmas plain, 10 of them were selected to predict the monthly groundwater level. Due to hydrogeological and morphological heterogeneous nature of this aquifer, the SOM-clustering method was used to classify the observation wells. This method produces three groups, labelled as G1, G2 and G3. Sugeno Fuzzy Logic (SFL) models were implemented for each of the three groups of observation wells, using the Subtractive Clustering (SC) technique by systematically increasing the cluster radius from 0 to 1. The input and output clusters were created using the Gaussian and linear membership function, respectively. Least Squares-SVM (LSSVM) type was applied to predict GWL. Selecting suitable kernel function and optimized value of kernel (γ) and regularization (C) parameters is important step in implementing SVM. The optimized value of γ and C were determined based on minimizing RMSE. The SFL and LSSVM models were built in three groups for predicting GWL at OW1-OW10 of the study area using training data. The three performance measures of RMSE, r2 and NSE for each of two models (SOM-SFL and SOM-LSSVM) at each of observation wells were calculated. The performance of SOM-SFL and SOM-LSSVM models during the testing was evaluated using the three performance measures for training the models and compared with the results obtained. This research investigated the hybrids of SOM-clustering method with SFL and LSSVM approaches for predicting groundwater level. Performance measures of these models indicate that their results of two hybrid models (SOM-SFL and SOM-LSSVM) are acceptable. In group one (G1) of observation wells (OW1 and OW7), SOM-LSSVM performs better than SOM-SFL. In OW2, OW6 and OW9, obtained results show that SOM-SFL model has better performance. The proposed hybrid models in this research (SOM-SFL and SOM-LSSVM) could successfully be used in predicting GWL. Combine the results of two models and using of multiple models can improve the final results.

Quantitative and qualitative conservation and prevention of water resources contamination are increasingly important. The existence and accumulation of elements in drinking water, which their chemical properties are stable, higher than the acceptance criteria can cause the wide range of diseases and is one of the most important factors in water contamination. Study of the Malekan plain hydrochemistry, distribution of rare elements in the aquifer and their effects on groundwater contamination in the study area, is the main aim of the present study. In order to assess the groundwater quality, 41 samples of groundwater resources were collected and analyzed. The high electrical conductivity in the west and northwest parts of the study area is due to high concentrations of gradual increase in major elements, through groundwater flow and the water quality has decreased from the recharging areas to the end regions of the plain. The result showed the heavy metals concentration such as Zinc, Chromium, Manganese, Iron and Aluminum were less than standard, in all samples. Arsenic contamination in the northwest part is the result of groundwater evaporation at the end of the plain. Fluorine concentration was less than both WHO guidelines and the official Iranian government standard for the chemical quality of water in all samples. Furthermore results showed reveal the effect of anthropogenic activities on groundwater quality, addressing the increasing of nitrate and boron concentration. Cation exchange, leaching of salty-clayey layers, human activities and groundwater evaporation at the end of the plain could be the main reasons for the groundwater quality reduction.

The coastal aquifer of the Naqadeh plain, located in the southwestern part of the Urmia Lake. Based on long-term chemoghraph, salinity has an increasing trend. Considering the undesirable results of saline water intrusion such as hygiene effects, human health and environmental problems, more studies are needed in this regard. Based on the classification of TDS values, about 48% of the groundwater resources of this plain are inappropriate for drinking and agricultural purposes. In order to monitor the groundwater resources of the plain, water samples were collected from 33 sampling points and a hydrochemical analysis was carried out. Identification of the salinity source and the governing hydrochemical mechanisms governing groundwater were carried out using the basic exchange index (BEX), ionic ratio, compound diagrams, geological and hydrogeological interpretations. Cl and Br halogens were also used for salinity studies due to their conservative behavior. Hydrochemical facies evolution diagram (HFE) was used as a useful method for identifying of the aquifers condition and its vulnerability to saline water intrusion. The matching of these results with the spatial distribution of salinity (EC) indicates the presence of two areas in the eastern and northern parts of the plain with high salinity and the risk of saline water intrusion. The salinity of these water resources is related to water-rock interactions, dissolution of saline formations, rainfall reduction, evaporation increase with formation of ponds in the dry seasons, infiltration of saline water and drainage of surface water, development of agricultural activities and probability of rising saline water (Upconing).

The present study demonstrates a method in groundwater quality assessment using the Empirical Bayesian Kriging and Moran Spatial Autocorrelation Index. In this study, concentration of Arsenic, Lead, Iron, Manganese and Nitrate and also groundwater table and Total Dissolved Solid has been measured for 21 point in Azarshahr Plain (East Azerbaijan). Azashahr study area is one of the Lake’s twelve adjacent aquifers that is located between 45°,46’ to 45°,50’ longitudinal and 37°,43’ to 37°,52’ latitudinal. Its total area is about 457 km2, that its plain has an area about 124 km2. The highest and lowest heights in the study area are 3700m and 1282m, respectively. Its average annual precipitation is about 221.2 mm whereas the average annual evaporation is about 1579 mm. The most important stream in Azarshahr Plain is Azarshahrchai which has a southeast-northwest trend and is eliminated before reaching to the lake because of wide agricultural usage. On the other hand, the total annual discharge of aquifer is about 90.64 million that is one of the groundwater depletion and decreasing the quality of groundwater factors in the study area. Moran’s I is a commonly used indicator of spatial autocorrelation. In this study, the Moran’s I was used as the first measure of spatial autocorrelation. Its value ranges from −1 to 1. The value “1” means perfect positive spatial autocorrelation (high values or low values cluster together), while “−1” suggests perfect negative spatial autocorrelation (a checkerboard pattern), and “0” implies perfect spatial randomness. After that, the appropriate weight has given to the aforementioned parameters, considering the international standard of water quality and spatial autocorrelation index of each of them. After determining the layer rules, the Expert Choice software was applied to calculate the comparing binary matrix of analytic hierarchy process. After that, the final weight for each layer with inconsistency of 0.08 was derived that is less than 0.1 and acceptable. In the conventional geostatistical approaches for interpolation and kriging, the variance structure is estimated first, and then the estimated variance is used for interpolation that whereas a Bayesian approach to the interpolation of spatial processes will provide a general methodology for taking into account the uncertainty about parameters on subsequent predictions. The Bayesian approach generalizes automatically to the case which the variogram parameters are unknown, whereas the classical approach essentially makes the assumption that these are known and only deals with the question of uncertainty of model parameters in a very peripheral way. Then replacing the popular interpolation methods, the Empirical Bayesian Kriging prediction method has utilized to expand every parameter to the whole plain. In order to evaluate the prediction results, the cross validation method was used. The study area was divided to 4 sections, as desirable, acceptable, moderate and non-acceptable. The final obtained map reveals that desirable quality is just located in the southeast of the study area in the upstream of the groundwater input. The acceptable quality of the groundwater is located in the east and southeast of the study area. The center, west and northwest of the study area has a moderate quality. The groundwater in the north, northwest and southwest of the study area has a non-acceptable quality that seems due to anthropogenic activities, especially agricultural and industrial during the recent years. On the other hand, the spatial autocorrelation model of effective parameters on water quality in the on hand and Bayesian kriging method with its precise assessment and prediction in some areas without data have a high applicability. The cross validation technique in model accuracy approving, is a valuable tool. Every three methods played an important role in modifying and improving the analytic hierarchy process of the groundwater quality assessment in the study area. The resulted map revealed that the groundwater quality of the east and southeast of the study area are desirable and acceptable with about 32.53 and 44.38 km2, respectively. The center and west section with area about 69.32 and the north and southwest with area about 27.21 km2 have a moderate and non-acceptable quality, respectively. Upon to the analytic hierarchy process of the groundwater quality assessment of the Azarshahr Plain, it has been revealed that 16 percent of aquifer has non-acceptable quality, about the 40 percent has moderate quality and other has an acceptable to desirable quality in the study area. The result of this study has shown the necessity of the groundwater quality precise monitoring in the study area.

In recent years, declining the water level of Lake Urmia has caused water and environmental crisis in the area. Therefore, it is urgent to carry out an accurate and reliable management and planning which requires modeling the lake's water level for the future. In this research, the artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) models were used to forecast the Lake Urmia water level fluctuations for one, two and three months ahead forecast horizons and finally, a supervised committee machine artificial intelligence (SCMAI) model was used to obtain a better performance than the used individual models. To develop the models, the current month [h (t)] and eleven months water level lags [h (t-1),…, h (t-11)] were introduced as input variables to forecast one, two and three steps ahead water levels. The datasets were divided into two subsets of training/validation (90%) and testing (10%). The performances of the models were evaluated based on the coefficient of determination (R2), the root mean square error (RMSE) and the mean absolute error (MAE). The results showed that the SVM models had better performance than the ANN and ANFIS models. The SCMAI model was applied to combine the used models’ outputs and illustrated that the SCMAI models are able to improve the performance of the individual artificial intelligence models. The results of the performance criteria for SCMAI model indicated that the one month step ahead water level modeling with R2, RMSE and MAE equal to 0.9896, 0.0547 m and 0.0421 m, respectively outperformed in comparison with SVM model which this performance is reliable for the two and three months step ahead lake's water level.

Due to groundwater overextraction, during the recent years, Malekan plain aquifer has been faced to a risk of increasing salination. The groundwater flow model of the plain was designed, invesitagion of groundwater flow regime and precise hydraulic parameters estimation, utilizing the various method such as  geological surveys, tomography methods, hydrochemical analysis and simulation of aquifer conceptual model in the GMS interface software package (GMS 10.1.4). The mass transport model was designed, then, to simulate the passways of salinity and dissolved solid concentration (Cl-) in the aquifer, using MT3DMS numerical code. The results show that the flow and transport model has explained the real behavior of the system with a good approximation and there is a good adaptation between the observed and simulated data. The over exploration of groundwater resources, which caused the water table decreasing,also resulted the extending of the saltwater intrusion in the northwestern part of  the plain and thereby the deteriorating of groundwater quality and quantity in northwestern part.

Limitation of surface water resources and excessive water utilization from aquifers, as well as the pollutants intrusion through the agricultural, urban and industrial activities, cause irreparable damage to groundwater. Groundwater system doesn’t respond quickly to contaminants and contaminants reaching time to the groundwater and its release into the aquifer is usually long. Remediation of contaminated groundwater and re-use often takes a lot of time and money and sometimes finding an alternative source of water is not possible. Therefore, the best and most effective solution is preventing the contamination entry to this valuable source. Determining the groundwater contamination level is one of the most important hydrogeological studies, which in this regard, the identification of susceptible areas and aquifer vulnerability assessment has great importance. Determining groundwater contamination is one of the most important hydrogeological studies. In this regard, identification of the contamination risk areas the aquifer vulnerability assessment has great importance. The Miandoab study area as a most important plain of the Urmia lake basin is one of the agricultural areas in Iran, especially in grape production, therefore, because of overuse of various chemical and animal fertilizers, it can be a nitrate-contaminated plain. Therefore, considering the importance of groundwater in the Miandoab region, which is also used for drinking, the vulnerability assessment of this plain is necessary. In the present study, the study of contaminant risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. 2. Materials and Methods The Miandoab Plain with an area of approximately 1150 Km2 is located in the south of Urmia Lake and is a part of the Alborz-Azarbayjan structural zone from the geological viewpoint. The average annual rainfall, based on the thirty years (1989-2018) data from Malekan and Miandoab synoptic stations is about 284 mm per year. This region, based on empirical Emberger method (1952) and using the statistics meteorology data, has a cold and semi-arid climate. Figure 1 shows the location of the study area. Fig 1. Geographic location of the study area The DRASTIC model has been used to mapping the groundwater vulnerability to pollution in many areas. Since this method is used in different places without any changes, it cannot consider the effects of pollution type and characteristics. Therefore, the method needs to be calibrated and corrected for specific aquifer and pollution. The DRASTIC model was improved with several methods such as the artificial neural network. In this study, the genetic algorithm is proposed for groundwater vulnerability.

The DRASTIC map was created by applying the weights for each parameter and integrating and overlaying the layers. According to the results of the DRASTIC model for plain, 15, 10, 17, 33 and 25 percent of the plain, respectively located in areas with very low, moderate, high and very high vulnerability. In the genetic algorithm method, the optimal weights of the parameters were obtained by maximizing the objective function. Based on the genetic algorithm method, groundwater depth, hydraulic conductivity, and unsaturated medium have the most effect on the vulnerability of groundwater in the region, respectively. Fig 2 shows the vulnerability map of Miandoab plain based on DRASTIC and optimized DRASTIC. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. A) B) Fig 2. Vulnerability map using: A) General DRASTIC, B) optimized DRASTIC using genetic algorithm

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

Estimating aquifer hydraulic parameters is essential for the assessment and management of groundwater resources. In this paper, the hydraulic parameters of Malekan Plain Aquifer were estimated using the resistivity data. The inverse electrical resistivity model to provide the best distribution of subsurface electrical resistance, using the inverse resistivity programs has been carried out. The relative thickness of subsurface layers using the characterization of electrical resistance curves was determined. The average thicknesse of, the alluvial aquifer and its porosity (ϕ) and specific yield (Sy) were estimated at 75 meters, 0.32 and 0.042, respectively. Results indicate a strong correlation between aquifer transmissivity and the transverse presence of resistance. The estimated values from both geoelectrical and pumping test methods indicate that the results of electrical resistivity tomography method are acceptable. Therefore, using suitable results may be obtained a combination of pumping test, geological studies and geophysical methods. The aquifer parameters obtained from the resistivity sounding and pumping test data may be used for an optimal management and assessment of groundwater resources.

As the most valuable natural resources, protection and management of groundwater is vital. One of the best ways to protect groundwater resources is identifying the aquifer vulnerable areas and monitoring groundwater quality spatial variations. The spatial variation of the groundwater salinity, has been evaluated in Malekan aquifer, introducing a new method, named GWQISI which has been digitally integrated of TDS, Cl-/sum Anion, Cl-/ (HCO3- + CO32-), Na+/ (Na+ + Cl-) and Ca2+/ (HCO3- + SO42-) ratios. Also, this paper presents the vulnerability mapping of the coastal aquifer system using new multi-criteria decision model as IDLRT. Five thematic layers have been created to adopt the most indicative criteria for investigating the groundwater degradation trends from saltwater intrusion. One of these layers showed the magnitude of existing status of saltwater intrusion. The other chosen layers are Recharge (R), Level of groundwater above the mean sea level (L), Distance from the shore (D) and the aquifer saturated Thickness (T). The results of sensitivity analysis, using both the map removal and single parameter methods, indicate that the Recharge (R) has the most significant impact on the vulnerability index. The groundwater vulnerability map indicates Based on the IDLRT results, about 22.15, 49.45 and 28.4 % of Malekan aquifer have very low, low and moderate vulnerability, respectively. Based on the GWQISI results, about 1.75, 4.25 and 62.4 km2 of Malekan aquifer are extermly, highly and moderately influenced by salinity, respectively. The results reveal that the northwest part is the most affected zone by salinity.

Shabestar plain is an active agricultural area and the utilization of groundwater resources is extremely important due to the shortage of surfaces water resources. Increasing of population and technological and agricultural activities possibly causes the aquifer contamination in this area. Therefore, assessing the groundwater vulnerability of this aquifer will be very useful for development, management and land use decisions, to monitoring of the groundwater resources quality and preventing the contaminations of groundwater resources. . In this study DRASTIC and SINTACS methods were used to assess the vulnerability of the Shabestan plain aquifer. Considering that the ratings and weights of the DRASTIC and SINTACS models are somewhat expertly Wilcoxon rank-sum test (WRST) method was used to improve the ratings in both the models and in order to optimize weights, simple statistical (SS) and genetic algorithm(GA) methods were used. Finally, the optimized WRST-SS-DRASTIC, WRST-GA-DRASTIC, WRST-SS-SINTACS, WRST-GA-SINTACS models were made. In all optimization models, the detemination coefficient between the nitrate concentration and the vulnerability index was increased relative to the original model. The higher determination coefficient of the WRST-GA-SINTACS model than other optimized models represents the better performance of this optimized model in the study area.

Increasing agricultural, industrial, and domestic demands as well as over abstraction of groundwater especially in semiarid areas like Iran is very important. Groundwater modeling in aquifers like Tabriz aquifer is vital for achieving the correct understanding of groundwater resources and also managing demands (agriculture, domestic and industrial) and engineering projects, as well as avoiding of groundwater declination. This research presents a hybrid model compose of self-organizing map (SOM) and artificial neural network (ANN) (SOM-ANN), for predicting groundwater level in the 10 observation wells which are located in unconfined Tabriz aquifer. The prevailing climate over Tabriz plain is semi-arid and cold, according to Emberger classification (Emberger, 1930). The average annual temperature is 13 °C (based on data from meteorological stations in Tabriz, 1986-2015) and the annual average rainfall is 251 mm as per data for the years of 1986-2015, recorded by the East Azerbaijan Regional Water Authority. Tabriz plain aquifer is composed of unconfined and confined aquifers with high heterogeneity and complexity. The unconfined aquifers overlay the confined aquifer and their composition comprises silt, clay, sand and gravel, which is repeated frequently, with varying thicknesses and grain size in different places. The bedrock of the confined aquifers is marl composed of clay and salt form Pliocene and Miocene. The unconfined aquifer in south of Tabriz is flanked to the hillside of Sahand mountains and the groundwater within this aquifer is freshwater; but the unconfined aquifer at the northern and western part of the plain is shallow (5 up to 58 m) and suffers from varying degree of salinity due to sedimentation originating from the Miocene Formation. The plain is drained by the Aji Chay River (the Aji River). The confined aquifer (58-120 m) has better quality (Nadiri, 2008) and is confined by marl. This study has focused on the unconfined aquifer component, which serves as the supply source of a significant proportion of drinking water in Tabriz. Based on data of 22 logs of drilled piezometers in the unconfined aquifer (prepared by East Azerbaijan Regional Water Authority, 2016), the saturation thickness in the central part of the plain is thicker than its margins. Unconfined aquifer of the plain has 42 piezometers and the general direction of flow is from the Northeast to the Southwest. In order to examine the effectiveness of the models in predicting groundwater levels, the performance measures were quantified for all models using Root Mean Squared Error (RMSE), Pearson correlation coefficient (R), Coefficient of determination (R^2). RMSE values range from 0 (the ‘perfect model’) to higher real numbers and measure global goodness-of-fit between the observed values and modelled value. The closer the value of RMSE to 0, the more accurate the prediction is. Pearson correlation coefficient and Coefficient of determination (0≤r^2≤1) describes the proportion of the total variance in the observed data that can be explained by the model. The amount of r^2 and r, with higher values, indicates better agreement between observed and predicted values. The SOM method was adopted to classify aquifer to homogeneous area, the ANN model utilized to predict groundwater level in each cluster. The 10 observation wells with monthly groundwater level data during 14 years were selected for this research. The observation wells were classified by SOM method into 4 classes. Effective parameters on groundwater variation were select as input data sets. The normalized five input parameters with one-month lag (t0-1) including precipitation (P), temperature (T), evaporation (E), withdrawal (Q) and the groundwater level (GWL) were applied to run the hybrid model. The SOM-ANN model is implemented for each class. According to the results of the models for Classes 1, 2 and 4, the average RMSE of the training and testing stages were obtained 0.10, 0.20, and 0.22 meters respectively, and for observation wells in Class 3 that are located in the center of the plain the average error was equal to 0.23 meter. The Higher error in model 3 or class 3 could be originated from increasing saturated thickness and complexity of hydrogeological in this area. The hybrid model error was acceptable to prediction of groundwater level variation. The aquifers are often complex like Tabriz aquifer, so, the SOM-ANN model is suggested as an applicable method to simplify other aquifers to decrease the errors of model.