New data mining and calibration approaches to the assessment of water treatment efficiency

Advances in Engineering Software

Volumn 44, Issue 1, 2012, Pages 126-135

  Bieroza, M ^a   Baker, A ^b   Bridgeman, J ^c  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF NEW SOUTH WALES   (Australia)

^c UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Artificial neural networks; Data mining; Fluorescence spectroscopy; Multivariate analysis; Organic matter removal; Pattern recognition

Indexed keywords

BACKPROPAGATION ALGORITHMS; BIOGEOCHEMISTRY; BIOLOGICAL MATERIALS; CHEMICALS REMOVAL (WATER TREATMENT); CONFORMAL MAPPING; DATA MINING; DATA REDUCTION; FLUORESCENCE; FLUORESCENCE SPECTROSCOPY; LINEAR REGRESSION; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; ORGANIC COMPOUNDS; PATTERN RECOGNITION; WATER TREATMENT;

MULTIPLE LINEAR REGRESSIONS; MULTIVARIATE ANALYSIS; MULTIVARIATE DATA; NUMERICAL ACCURACY; ORGANIC MATTER REMOVAL; PARAFAC; PARALLEL FACTOR ANALYSIS; PARTIAL LEAST SQUARES; PRINCIPAL COMPONENTS ANALYSIS; ROBUST DATUM; SELF ORGANIZING; STEPWISE REGRESSION; TREATMENT EFFICIENCY; TREATMENT PERFORMANCE; WATER TREATMENT WORKS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 80955151470     PISSN: 09659978     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advengsoft.2011.05.031     Document Type: Conference Paper

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