Mapping Aquifer Vulnerability Indices Using Artificial Intelligence-running Multiple Frameworks (AIMF) with Supervised and Unsupervised Learning

Water Resources Management

Volumn 32, Issue 9, 2018, Pages 3023-3040

  Nadiri, Ata Allah ^a   Gharekhani, Maryam ^a   Khatibi, Rahman ^b  

^a UNIVERSITY OF TABRIZ   (Iran)

^b GTEV ReX Limited   (United Kingdom)

Author keywords

Aquifer vulnerability; Ardabil plain; Artificial Intelligence (AI); Prescriptive DRASTIC framework; Unsupervised Multiple Frameworks (MF)

Indexed keywords

AQUIFERS; ARTIFICIAL INTELLIGENCE; GENETIC ALGORITHMS; NITRATES; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES; UNSUPERVISED LEARNING;

ANTHROPOGENIC CONTAMINATION; AQUIFER VULNERABILITY; ARDABIL PLAIN; COEFFICIENT OF CORRELATION; PRESCRIPTIVE DRASTIC FRAMEWORK; SUPERVISED AND UNSUPERVISED LEARNING; THEORETICAL FORMULATION; UNSUPERVISED MULTIPLE FRAMEWORKS (MF);

GROUNDWATER POLLUTION;

AQUIFER; ARTIFICIAL INTELLIGENCE; GENETIC ALGORITHM; SUPERVISED LEARNING; SUPPORT VECTOR MACHINE; VULNERABILITY;

ARDABIL; IRAN;

EID: 85044219013     PISSN: 09204741     EISSN: 15731650     Source Type: Journal    
DOI: 10.1007/s11269-018-1971-z     Document Type: Article

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