Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM).
This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran. Electronic address: nadiri@tabrizu.ac.ir.
Ata Allah
AA
Nadiri
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran. Electronic address: m_gharakhani92@ms.tabrizu.ac.ir.
Maryam
M
Gharekhani
GTEV-ReX Limited, Swindon, UK. Electronic address: gtev.rex@gmail.com.
Rahman
R
Khatibi
Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, East Azerbaijan, Iran. Electronic address: s.sadeghfam@maragheh.ac.ir.
Sina
S
Sadeghfam
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran. Electronic address: moghaddam@tabrizu.ac.ir.
Asghar Asghari
AA
Moghaddam
Ardabil aquifer; Artificial intelligence models; Nitrate; Supervised Intelligent Committee Machine; Vulnerability index