supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

• Published in: Stochastic environmental research and risk assessment Vol. 30; no. 3; pp. 883 - 899
• Main Authors: Barzegar, Rahim, Moghaddam, Asghar Asghari, Baghban, Hamed
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016; Springer Nature B.V
• Subjects: anthropogenic activities; Aquatic Pollution; Aquifers; Artificial intelligence; Case studies; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Contamination; data collection; Earth and Environmental Science; Earth Sciences; Environment; Geology; Groundwater; groundwater contamination; Groundwater pollution; Hydrogeology; Learning theory; Math. Appl. in Environmental Science; Mathematical models; Neural networks; Original Paper; Physics; prediction; Probability Theory and Stochastic Processes; Risk; Risk assessment; Statistics for Engineering; system optimization; Training; Waste Water Technology; Water analysis; Water Management; Water Pollution Control; Water sampling; Iran; Groundwater contamination risk; Artificial intelligent models; Iran; Tabriz plain; DRASTIC

Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for...