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.
