فهرست مطالب esfandiar abbas novinpour

Assessing water quality is an important step toward the optimal and appropriate use of drinking water resources. Therefore, the necessity of studying water quality characteristics in water resource management programs has been highly considered. Ambiguity and lack of inherent certainty governing water resources in the evaluation of goals, criteria, and decision-making units, as inconsistency and carelessness in the opinions and judgments of decision-makers have led to the tendency towards theories of fuzzy sets and, as a result, fuzzy logic as an efficient and useful tool for planning and making decisions. In the present underground water quality water was first classified by international standard methods (definitive evaluation method) for drinking purposes. Then classification was modeled and compared using Mamdani fuzzy inference. For this purpose, the four-year average of quality parameters of underground water sources related to 33 sources including 10 well rings, 22 spring mouths, and an aqueduct in operation in Sardasht Cityre used as inputs in two cases. In the deterministic evaluation method (Schoeller diagram), the characteristics and the water quality determination diagram were determined. In the four-year average fuzzy inference model, eight water quality parameters were classified into three groups, in the first group the parameters of Na+, Ca+2, and Mg+2, in the second group the parameters of HCO3-, SO4-2, and Cl- were placed in the third group of two TH and TDS parameters. After determining each group with two input parameters, each input parameter was considered including three membership functions, so that the rules considered for it were estimated as nine (3x3). The results based on the deterministic method showed that all the studied samples were in the good to acceptable group. But Mamdani's fuzzy findings showed that two samples with a confidence level of 50% were in the acceptable category and other samples with a confidence level of 83-87% were placed in the desirable category for drinking.

Qorveh catchment is located between Dehgolan and Chahar Doli catchments in Kurdistan province and 95 km east of Sanandaj and northwest of Hamadan. The general direction of ground water flow is mainly from southwest to northeast and in the direction of river flow. The groundwater level map of the study area shows that most parts of the alluvial plain area are not very exploitable to the extent that in some areas it shows a drop of up to 2.5 meters. Aquifer groundwater quality was evaluated using the results of data analysis of 18 wells in a statistical period of 15 years for the maximum (June) and minimum water table (October). In general, according to studies conducted in most parts of the plain, groundwater quality is good and acceptable for various uses. Water budget elements are calculated by different methods and based on the analysis of the volume of input and output elements from water budget area shows that this area is 47.6 MCM of water per year through the infiltration of rainfall, adjacent aquifers, runoff and floods. Wells, springs and canals enter and annually 53.01 MCM through evapotranspiration, operation and pumping of wells, discharge from canals, springs discharged. Studies and balance calculations show, from the viewpoint of discharge of groundwater resources with the problem.

Increasing the number of water quality monitoring stations and conducting multiple sampling is one of the measures to improve the productivity of river water quality control operations. The figures must be determined by scientific methods so that the system dynamism can be explained. Increasing rivers pollution, which sometimes oversteps the self-purification limitation of the rivers, causes great damage to the environment. In the present study, while modeling the behavior of the river in the conditions of effluent entry whether point or non-point, multiple criteria decision-making method (AHP) was conducted to prioritize monitoring operations at river stations. For this purpose, the Gedarchay River was chosen as a case study. Based on the WQI results, water quality parameters rose alongside the river with rural sewer flowing. As such, stations 7 and 8 in scenario I showed moderate quality due to increased pollution loading while other stations and by extension the other scenarios indicated good to excellent figures. Considering water quality monitoring operations (), the existing stations were prioritized. The weights of the main criteria were determined as follows C1 = 0.483, C2 = 0.157, C3 = 0.272 and C4 = 0.088, and the stations 1-8 were weighted respectively 5.1, 7.9, 8.6, 16.6, 16.3, 17.5, 16.2 and 12.0, confirming the importance of station 6 in getting prioritized.

The studied aquifer is located in Zarandieh Plain of Markazi Province with an area of about 1200 square kilometers. Therefore, in order to identify and protect the optimal use of groundwater resources, a mathematical model of the aquifer was constructed using the MODFLOW code under the 7.1 GMS interface. For this purpose, first a conceptual model is prepared and then a numerical model based on it, based on water level changes using water table statistics of 14 piezometric wells in the region, in a time step of one month for steady state (October 2010) and a period of 12 months (November 2010 Until October 2011) for unstable state, was calibrated manually and automatically. During this stage the initial values of hydraulic conductivity (K) was optimized. At the end of calibration, in the steady state, the RMS amount reached 1.265 meters and in the unstable state, the RMS amount reached 2.145 meters, which is less than the allowable error value of ± 2.5 meters and is acceptable. Also, the validity of the model was performed in another period of 12 months (November 2011 to the end of October 2012) that at the end of validation, the RMS value was calculated to be 2.46 m. Due to the appropriateness of the RMS values of the simulated model, it has the ability to predict the future status of the aquifer. Then, after confirming the accuracy of the model in the validation phase, the future conditions of the aquifer were predicted and the results showed that in the next 3 years, with the continuation of the current trend of harvesting from exploitation wells, in some parts of the aquifer will decline.

Recently, land subsidence has become one of the serious environmental problems due to increased agricultural and industrial activities. Population growth have caused to groundwater over abstraction and land subsidence in some parts of country such as Ardabil plain. Therefore, identifying, control and management of high potential subsidence areas may help to better understand this complex phenomenon and avoiding the possible damages. In this research, a new framework suggested using seven effective parameters on subsidence to determine areas that are vulnerable to land subsidence and its capability is evaluated in Ardabil plain aquifer. In this framework seven effective parameters on land subsidence including groundwater level decline, aquifer media, recharge, pumping, land use, alluvium thickness and fault are prepared in raster layer format and weights and rates assigned for layers to calculate Subsidence Potential Index (SPI). The SPI for Ardabil plain aquifer was obtained from 80 to 154. According to inherently uncertainty of the assigned weights by expert, genetic algorithm adopted to optimize given weights. The results were compared with subsidence value obtained from radar images and it indicated that optimized framework have relatively better results. The southern and southeastern parts of the Plain shows higher SPI.

Ahar aquifer is one of the most active agricultural and livestock areas. The development of these activities has led to an increase in the use of chemical and natural fertilizers. Therefore, in this research, groundwater vulnerability in Ahar aquifer has been investigated using three frameworks; DRASTIC, SINTACS and GODS to identify vulnerable areas and prevent groundwater resources from more contamination. To use the simultaneous advantage of all three frameworks, two unsupervised and supervised combination techniques were applied to combine them. Invalidation step, nitrate concentration data and its correlation coefficient with the vulnerability index were used. The results show that the artificial neural networks (ANNs) model has better performance with a better determination coefficient (R2) and correlation coefficient (CI) than other techniques. Therefore, the supervised method has a higher ability than the weighted averaging method and can be used to determine the vulnerability of other areas.

Population growth, agricultural and industrial development has led to a sharp increase in water use, a decrease in the quantity, quality and pollution of groundwater resources. Appropriate measures to prevent pollution of groundwater resources are the identification of vulnerable aquifers. In this study, combined method (DSM) is the combination of DRASTIC and SINTACS methods, was used to evaluate the vulnerability of Khoy plain aquifer. Both methods use hydrological and hydrogeological parameters of this area to investigate the aquifer vulnerability and zoning of susceptible areas, but each of these methods has inherent characteristics and presents different results. Therefore, in this study due to the similarity of the results of these two. The method has been used simultaneously using combination of their advantages. Validation of each of methods were carried out with measured nitrate values from 26 wells in the study area. The Correlation Index (CI) was calculated between vulnerability maps and nitrate values to validation and compare these methods. The results showed that the CI of combined method is high; therefore, this method is more suitable for assessing the vulnerability of this region. Based on the results of the combined method, 19, 42 and 39 percent of Khoy plain aquifer are located within low, medium and high vulnerable areas.

Most of the country's geographically area is located in dry and semi-dry zone with low rainfall. The growing population, the limitation of water resources and the prevalence of groundwater resources in most parts of the country requirement to accurate prediction of the amount of these resources due to the importance of these resources in optimal planning and management. In this research, in order to estimate the fluctuations of groundwater level in the Baruq aquifer, the artificial intelligence models including fuzzy, support vector machine and neural network models were used by the data of depth from 7 piezometers with long-term data of 14 years, as well as changes in temperature and precipitation in this period. Despite the inherent abilities of each models in predicting groundwater level, the heterogeneity of the study area prevented the high efficiency of these models. Therefore, SOM-AI modeling combined the self-organized maps (SOM) classification method and each model that is increased the efficiency of each composite model in different parts of the aquifer by dividing the study area into homogeneous regions. The results showed that the proposed method can be an effective method in the modeling of heterogeneous and even multi-layered aquifers.

Recently, land subsidence due to natural and human factors changed to catastrophic destruction for the residential, agricultural and industrial areas. In this study, the high potential subsidence areas of Ardabil plain were identified to control and manage this phenomenon. Thus, the seven effective parameters on the subsidence were rated and weighted and the subsidence potential index (SPI) was calculated for study area. The SPI value for the Ardabil Plain was obtained between 80 and 154. Then the results of this method were verified by occurred subsidence which is obtained through INSAR image analysis for the study area. Although the results are acceptable but to increase the framework efficiency and accuracy of results, the fuzzy model adopted to optimize this framework. In this way, seven effective parameters on the SPI were used as the input of the FL model and the value of the corrected SPI by obtained observed subsidence from INSAR images was defined as the output of the model. The results of this method showed that the southern and southeastern parts of the plain have the high subsidence potential.

Miandoab Plain is part of the Urmia Lake basin, which has significantly reduced the quantity and quality of its water resources in recent years. Groundwater vulnerability assessments provide a useful method for analyzing the sensitivity of aquifer to the contaminant. The purpose of this study was determining the vulnerability of the aquifer using AVI, GODS, DRASTIC and modified DRASTIC models so that the vulnerability potential to pollution can be determined more exactly. In this study, the data and information provided by the Regional Water Company of East Azarbaijan were used to provide layers needed for the preparation of each vulnerability model in the ArcGIS software. The new rates of modified DRASTIC model calculated using a correlation between each parameter and the nitrate concentration obtained from the analysis of 16 water samples taken in July 2016. The final mapping of the vulnerability maps was also verified by nitrate values. Also, validation of the final vulnerability maps was done with nitrate values. The coefficients of determined values between the nitrate concentration and vulnerability models of AVI, GODS, DRASTIC and modified DRASTIC were estimated to be 0.004, 0.13, 0.18, and 0.33, respectively. The results showed that after calibrating the model with the nitrate data, it is the best model for assessing the vulnerability of Aquifer. Based on this, it was observed that the most vulnerable groundwater pollution is in the northeast and parts of the west of the plain and the least vulnerable in the southern and southeastern parts of the plain.

The decline of water table is very important in management point of view and might cause negative impacts such as land subsidence, raising costs and reducing groundwater quality. Groundwater is the most important source of water supply in Dehgolan plain. Increasing water requirements and extractions, has declined water table. This plain with an area of about 780 km2 is one of the protected plains of the Kurdistan province and with decrease in water table about 37 meters, it has the most decline between plains of the province. The purpose of this study is to model the groundwater level and compare the performance of the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Inverse Distance Weighted (IDW), Kriging and Cokriging methods. For this purpose, in September 2016, the water table data relating to the 44 Piezometer digged in Dehgolan plain has been used to modeling. The results show that the hydraulic head behavior is different across the aquifer, so the use of spatial data (h) for modeling doesn’t lead to satisfactory outputs. The water table in Dehgolan plain has the highest correlation with topography conditions and the ANFIS with a RMSE = 0.07, MSE = 0.005, MAE = 0.06, MBE = 0.04 and = 0.88 R2, has presented better performance than other methods.

  Population growth and the development of the agriculture and industry and the excessive use of groundwater resources have caused a drop in the water level. In arid and semi-arid areas, aquifer water management plays an appropriate role within human health of river basins and, therefore, their protection from anthropogenic contamination sources can be managed by proactive tools based on the aquifer vulnerability indices. The groundwater system does not respond quickly to contaminants. The arrival and diffusion of pollutants to groundwater occurs over time. Groundwater contamination is identified using the aquifer to provide water. Consequently, complete elimination of pollution is a long and often impossible process.The concept of vulnerability was first introduced in the late 1960s in France to provide information on groundwater contamination. The SINTACS framework is a suitable prescriptive approach but despite its popularity, it is susceptible to the need for expert judgment on assigning weights and rates for each parameter, which expose the output vulnerability maps to uncertainties in the same study area. Among different AI techniques, the current study was based on Mamdani Fuzzy Logic (MFL) to remove the expert opinion applied to SINTACS indices. The Bilverdi sub-basin, with an area of 289 km2, is located approximately in 65 km of Tabriz city, East Azerbaijan, Iran. There is a vallilu arsenic mine to the north of Bilverdi plain. There are 208 wells, 7 springs, and 17 qanats in the study area.There is a possibility that the mine drainage leaks into the water resources and also extensive agricultural activities in the region increase the need to evaluate the vulnerability of the Bilverdi plain. In this study, SINTACS methods were used for the assessment of the inherent vulnerability of the Bilverdi plain aquifer. The SINTACS method is a PCMS which was developed by Civita and De Maio(2004) in order to assess the intrinsic vulnerability of groundwater with an increasing weight parameters and the wider range of ratings than the DRASTIC method. The acronym SINTACS originates from Italian words. The SINTACS method uses seven effective environmental parameters including Soggiacenza (depth of water), Infiltrazione efficace (effective infiltration), Non saturo (vadose zone), Tipologia della copertura (soil cover), Acquifero (aquifer), Conducibilità idraulica (hydraulic conductivity), and Superficie topografica (slope of topographic surface) to assess the vulnerability of the aquifer. After assigning weight and rate in the ArcGIS software, it was prepared as raster layers. Then SINTACS optimization was performed using Mamdani Fuzzy Logic (MFL). In this research, for the first time, the SINTACS method was optimized with artificial intelligence methods. Seven layers of the SINTACS method as an input and the SINTACS index corrected with nitrate were selected as the output model.


The SINTACS vulnerability Index Obtain by overlaying these seven layers and the Mamdani Fuzzy Logic (MFL) were used to optimize the SINTACS method and the data was divided into two categories of train and test. After model training, the model results were evaluated by the nitrate concentration through coefficient of determination (R2) and correlation index (CI) criteria. The results are as follows: The SINTACS Vulnerability Index was estimated to be between 70 and 169, of which 30, 67 and 3% of the study area were respectively located in low, medium, and high vulnerability zones.The results of the validation of the vulnerability maps with measured nitrate concentrations showed a correlation index (CI = 29). The results of the Mamdani Fuzzy Logic (MFL) were respectively R2 = 0.9, RMSE = 5.1 and R2 = 0.85, RMSE = 7.79 in the training and testing stages. The Vulnerability map of the numerical index is between 167.23 and 88.94 and the correlation index was (CI = 31). This study used the SINTACS framework to assess groundwater vulnerability for Bilverdi basin, East Azerbaijan, Iran. The combined use of the SINTACS method and the geographical information system (GIS) produced a useful groundwater vulnerability map. The SINTACS index was calculated from 70 to 169. The poor determination coefficient calculated by the basic SINTACS framework made a research case for the application of Mamdani Fuzzy Logic. The results showed that Mamdani Fuzzy Logic (MFL) model showed high capability to improve the results of the general SINTACS and reduced the subjectivity of the model. The most vulnerable areas were in the northeast and southwest plain. The high vulnerability area needed to adopt strategic plans and policies to prevent the pollution of aquifers.

Groundwater system is a complex and heterogeneous system and estimation of hydrogeological parameters with classical methods such as laboratory methods, slug test, tracing and pumping tests are associated with inherent uncertainties and are costly and time consuming. Therefore, artificial intelligence methods have been used to hydraulic conductivity estimation, can reduce the uncertainty of this variable and increase the accuracy of calculation for overcoming to defects of classical methods. In the meantime, the optimal management of this demand and the prevention of aquifers destruction and groundwater resources, requires the accurate recognition of hydrodynamic parameters. In this study, Sugeno fuzzy logic (SFL) and Mamdani fuzzy logic (MFL models) were used to hydraulic conductivity estimation of the Barug aquifer. In this regard, the data of electrical conductivity (EC), saturation zone thickness (B) and transverse aquifer resistance (RT) were used as input data of the model. Finally, in order to obtain the most ideal model, the RMSE and R2 were calculated for both models and the two models were compared. The results indicate that Sageno fuzzy model had a better performance with coefficient function and root mean square error being receptively R2=0.94 and RMSE=0.045 in comparison with mamdani model for estimating hydraulic conductivity.

Increasing growth of population leads to increase in human activities such as agriculture and industry, which these activities increasing and irregular usage of fertilizers, pesticides and insecticides result in soil and groundwater pollution. Meanwhile, vulnerability assessment can play important role in the management of polluting activities. In this research a new method has been proposed for the vulnerability assessment of Moghan plain as one of the most important provider of agricultural products and animal husbandry in the Ardebil province. This method incorporates the weighted combination of three common vulnerability assessment methods DRASTIC, SINTACS and SI which. Comparison of obtained results from the proposal method with field nitrate concentration data of this area which sampled from 21 tube wells in the study area in the autumn 1394 indicates that the proposed method has a higher correlation index than other methods. According to the result of proposed method, 41, 46 and 13 percent of the study area have been located in areas with low, medium and high vulnerability respectively.The proposed methodology in this study could be used for vulnerability assessment of other aquifers.

Salmas plain is located at the west of Uremia lake and at the 75 km of north of Uremia city. The main river of the plain, called Zulachai, is drained into Uremia Lake. Salmas aquifer is one of the coastal aquifers of Uremia Lake and has hydraulic connection with the lake. The recent draught occurrences and increased water consumption in agriculture and drinking sections water have not only caused regression of lake water, but also effected on coastal aquifer, such as Salmas plain aquifer. Over the long-term, average groundwater level in the Salmas aquifer has dropped sharply to 18/05 meters (averaging about 0/44 meters per year) and there has been 241 million cubic meters of reservoir capacity reduction. Salmas plain has two confined and unconfined Aquifers. Salmas annual depletion of groundwater resources in the study area during the water years of 2014-15 has been equal to 146 million cubic meters. The effects of land subsidence have been observed in some output areas (Kalshan and Qareh Qeshlaq areas).