Optimization of the DRASTIC and SINTACS Models in Assessing the Vulnerability of the Shabestar Plain Aquifer

Document Type : Research Article

Authors

1 MSc. Student in Hydrogeology, Faculty of Natural Sciences, University of Tabriz

2 Professor in Hydrogeology, Faculty of Natural Sciences, University of Tabriz

3 Ph.D. Student in Hydrogeology, Faculty of Natural Sciences, University of Tabriz

Abstract

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 determination 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.

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Main Subjects

References

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Iranian journal of Ecohydrology
Volume 6, Issue 1
April 2019
Pages 77-88

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History

  • Receive Date: 23 August 2018
  • Revise Date: 24 November 2018
  • Accept Date: 24 November 2018
  • First Publish Date: 21 March 2019
  • Publish Date: 21 March 2019

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