Scientific Quarterly Journal of Geosciences

Scientific Quarterly Journal of Geosciences

Investigating the salinity vulnerability potential of Ajab-Shir plain aquifer using GALDIT framework and improving it with artificial intelligence

Document Type : Original Research Paper

Authors
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran
Abstract
In recent years, the salinization in coastal aquifers, where agriculture is always developing, requires the investigation of aquifer vulnerability to water salinity. In the Ajab-Shir aquifer, to investigate the GALDIT vulnerability framework, which includes six layers such as type of aquifer (G), Aquifer hydraulic conductivity (A), Groundwater level above the sea level (L), distance from the coastline (D), quality Impact of saline water intrusion (I) and Aquifer thickness (T) used. Meanwhile, TDS values used to validate zoned vulnerability map. Also, to ensure the certainty of the available data, as well as to improve the weight and fix expert's errors in determining the weight of GALDIT layers, fuzzy logic (Sugeno), genetic algorithm (GA), random subspace algorithm (RS) and decision tree algorithm (M5P) were used. The results showed the correlation coefficient of about 0.5, 0.81, 0.6, 0.8, and 0.8 between GALDIT, GALDIT-F, GALDIT-GA, GALDIT-RS, and GALDIT-M5P with TDS, respectively. The south and south-east parts of the plain show the highest salinity potential and correlation values showed the good performance of GALDIT-F, GALDIT-RS and GALDIT-M5P methods in this study.
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