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
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-015-1088-3

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 evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze [Formula: see text] concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the [Formula: see text] data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using [Formula: see text] concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and [Formula: see text] concentration values. After model training, the AI models are verified by the second [Formula: see text] concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.