Machine learning models for predicting PAHs bioavailability in compost amended soils

Chemical Engineering Journal

Volumn 223, Issue , 2013, Pages 747-754

  Wu, Guozhong ^a,b   Kechavarzi, Cédric ^b   Li, Xingang ^a,c   Wu, Shaomin ^b   Pollard, Simon J T ^b   Sui, Hong ^a,c   Coulon, Frédéric ^a,b  

^a TIANJIN UNIVERSITY   (China)

^b CRANFIELD UNIVERSITY   (United Kingdom)

^c National Engineering Research Centre for Distillation Technology   (China)

Author keywords

Bioavailability; Compost; Cyclodextrin extraction; Machine Learning; PAH

Indexed keywords

BIOAVAILABILITY; COMPOST; CROSS-VALIDATION METHODS; ENVIRONMENTAL ASSESSMENT; MULTI-LAYER PERCEPTRONS (MLPS); POLYCYCLIC AROMATIC HYDROCARBONS (PAHS); RADIAL BASIS FUNCTION(RBF); SUPPORT VECTOR REGRESSION (SVR);

BIOCHEMISTRY; COMPOSTING; FORECASTING; LEARNING SYSTEMS; POLYCYCLIC AROMATIC HYDROCARBONS; RADIAL BASIS FUNCTION NETWORKS; REMEDIATION; SOIL CONSERVATION; SOIL MOISTURE; SOIL POLLUTION;

GEOLOGIC MODELS;

EID: 84876335365     PISSN: 13858947     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cej.2013.02.122     Document Type: Article

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