Comparison of different combination methods ability on groundwater vulnerability assessment in Qorveh- Dehgolan palin aquifer

Document Type : Research Article

Authors

1 Assistant Professor of Natural Faculty, University of Tabriz

2 Department of Earth Sciences, Natural Sciences, University of Tabriz

3 Department of Earth Sciences, University of Tabriz,

Abstract

Qorveh-Dehgolan plain is the largest plain of Kurdistan province, which is the most active agricultural area in the province. In recent years, agricultural development has tended to increase the use of chemical fertilizers and has caused aquifers in the plain to be contaminated. Therefore, it is necessary to determine vulnerable areas for managing areas at risk. In this research, the aquifer vulnerability of Qorveh-Dehgolan plain has been investigated using DRASTIC, SINTACS and SI methods, which is one of the most common ranking methods for assessing aquifer vulnerability. Considering that each of these methods has its own advantages, these methods were combined using Sagnuo fuzzy model, genetic algorithm and combined method as an unsupervised and supervised combination method. The results showed that correlation index of all three combined methods is more than single methods (DRASTIC, SINTACS and SI). In combination methods, the method of Sagnuo fuzzy model combination has the highest correlation index, and the correlation coefficient of this method is higher than the other combination and single methods. Therefore, the combination method of Sagnuo fuzzy model is a better method for assessing the of Qorveh-Dehgolan aquifer than other methods. Based on this method, about 21.5%, 49.4%, 24.7% and 4.4% of the aquifer are in the low, medium, high and very high vulnerability, respectively. The north-west and south-east parts of the plain have more potential pollution than other parts of the plain, and more protection from these areas needs to be done.

Keywords

Main Subjects


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Volume 6, Issue 3
September 2019
Pages 821-836
  • Receive Date: 18 April 2019
  • Revise Date: 21 June 2019
  • Accept Date: 21 June 2019
  • First Publish Date: 23 September 2019
  • Publish Date: 23 September 2019