Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM)

Science of the Total Environment

Volumn 574, Issue , 2017, Pages 691-706

  Nadiri, Ata Allah ^a   Gharekhani, Maryam ^a   Khatibi, Rahman ^b   Sadeghfam, Sina ^c   Moghaddam, Asghar Asghari ^a  

^a UNIVERSITY OF TABRIZ   (Iran)

^b GTEV ReX Limited   (United Kingdom)

^c UNIVERSITY OF MARAGHEH   (Iran)

Author keywords

Ardabil aquifer; Artificial intelligence models; Nitrate; Supervised Intelligent Committee Machine; Vulnerability index

Indexed keywords

AQUIFERS; ARTIFICIAL INTELLIGENCE; FUZZY NEURAL NETWORKS; GENE EXPRESSION; GROUNDWATER; GROUNDWATER POLLUTION; HYDROGEOLOGY; NEURAL NETWORKS; NITRATES; SUPPORT VECTOR MACHINES; UNCERTAINTY ANALYSIS;

AI TECHNIQUES; COMMITTEE MACHINES; GENE EXPRESSION PROGRAMMING; GROUNDWATER VULNERABILITY; HYDROGEOLOGICAL PARAMETERS; INDEX VALUES; UNCERTAIN PARAMETERS; VULNERABILITY INDEX;

GROUNDWATER RESOURCES;

GROUND WATER;

AQUIFER; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; FUZZY MATHEMATICS; MODEL TEST; NITRATE; PERFORMANCE ASSESSMENT; POLLUTION INCIDENCE; SUPPORT VECTOR MACHINE; VULNERABILITY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CALCULATION; ENVIRONMENTAL TEMPERATURE; FUZZY SYSTEM; GENE EXPRESSION PROGRAMMING; GEOLOGY; LAND USE; MACHINE LEARNING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; RELIABILITY; SUPERVISED INTELLIGENT COMMITTEE MACHINE; SUPPORT VECTOR MACHINE; WATER CONTAMINATION; WATER POLLUTION; WATER QUALITY;

ARDABIL; IRAN;

EID: 84988329891     PISSN: 00489697     EISSN: 18791026     Source Type: Journal    
DOI: 10.1016/j.scitotenv.2016.09.093     Document Type: Article

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