Comparison of different combination methods in simultaneous use of frameworks abilities to assess groundwater vulnerability

Authors

1 Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Iran

2 Department of Geology, Faculty of Sciences, University of Urmia, Iran

3 Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran

Abstract

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.

Keywords

References

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Advanced Applied Geology
Volume 10, Issue 2
July 2020
Pages 252-270

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