mehrdad hassanzadeh; mehdi momeni reghabadi; amir robati
Abstract
1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment ...
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1-IntroductionGroundwater pollution is one of the most serious and important issues in urban and agricultural areas due to land use. For this purpose, in order to obtain methods and garbage water from the pollutants that removes them, the use of methods for garbage water vulnerability assessment such as AVI, GODS, DRSTIC, SINTACS, etc. were developed. Intrinsic vulnerability is assessed according to the hydrological and hydrogeological characteristics of the region, such as the characteristics of the aquifer and the stresses imposed on it. Occurs with inherent vulnerability components. The most common methods of assessing vulnerability index include DRASTIC, GOD, SINTACS, SI and AVI rating methods. In this study, the vulnerability of the aquifer has been investigated using DRASTIC and SINTACS models, and in order to validate the results of the methods used, electrical conductivity concentration data were used. 2-MethodologyHajiabad plain is located 160 km north of Bandar Abbas and between 35, 55 to 00 and 56 longitudes and latitudes 17, 28 to 21 and 28 north, from the north to the heights of Bibi Dokhtaran mountain from the west to Sirjan-Bandar Abbas road from To the east to the heights of Anfuzeh mountain and from the south to the congomara hills and the average width is 4 km. The climate of the region is warm and the average temperature of the region is 19.8 degrees Celsius and the average annual evaporation of the plain is 2464.7 mm. In order to study the hydrochemical properties of groundwater in the region, 16 samples of water analyzed from groundwater study wells by the Regional Water Organization of West Azerbaijan Province for the water year 93 were used.3- Results and DiscussionVulnerability maps of Drastik and SINTACS models were prepared by applying weights related to each parameter and combining layers using the overlap function. According to the SINTACS map, the vulnerability of the plain is estimated from 115 to156, the plain is in the range of medium, medium to high and high vulnerability. According to the vulnerability classification with SINTACS model, it shows that parts of the center of the plain (near Aliabad and Hajiabad villages) are in the upper floor and the northern slope of the Hajiabad plain basin has the middle floor. Most of the plain area was in the range of moderate to high vulnerability. The results showed that the Syntax model has more flexibility than the Drastic model and the probability of vulnerability is slightly higher than the Drastic model. The final map of Drastik model estimated the vulnerability of the plain from 94 to 128. The highest vulnerability is in parts of the center of the plain (near Aliabad and Hajiabad villages) and the lowest in the northern slope of Hajiabad plain basin and according to the range of Drastic vulnerability index provided by Aller Et al, (1987), vulnerability of the region is divided into 3 categories between low to medium risk. In order to study more closely and also to compare the classical methods used in this study, the method of calculating the correlation index (CI) in the aquifer and electrical conductivity data were used. For this purpose, electrical conductivity values were divided into three categories of low, medium and high electrical conductivity. Adaptation of wells with three levels of EC pollution and vulnerability categories predicted by DRASTIC and SINTACS methods was brought for Hajiabad aquifer. Based on the value of the correlation coefficient between the map produced using the drastic model with the electrical conductivity map, 39 and the same value was obtained for the Syntax model 35, which are slightly different from each other.4-Conclusions In this study, both drastic and syntactic methods predicted the potential risk in Hajiabad aquifer with almost equal accuracy. Having the correlation index between the electrical conduction point data and the vulnerability map, it showed that the Drastic model provided better vulnerability than the SINTACS model. Contamination potential in both studied models is low in the northern and southern regions. This can be due to high groundwater depth and low hydraulic conductivity. Comparing the models with the coefficient of determination between the electrical conductivity concentration and the vulnerability parameters showed that the highest correlation was in the slope layer, depth to the water table and the material of the unsaturated medium.Keywords:Aquifer vulnerability, SINTACS Method, Groundwater, Hormozgan5-References Aller, L., T. Bennet, J.H. Lehr, R.J. Petty, and G. Hackett. (1987). DRASTIC: a standardized system for evaluating groundwater pollution potential using hydrogeological settings. EPA/600/2–87/035. US Environmental Protection Agency, Ada, OK, USA.
AtaAllah Nadiri; Esfandiar Abbas Novinpour; Rana Faalaghdam; Zahra Sedghi
Volume 5, Issue 17 , March 2019, , Pages 103-123
Abstract
Introduction
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, ...
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Introduction
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.
Materials and Methods
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.
Results and Discussion
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).
Conclusion
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.
