Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain)

Environmental Research

Volumn 122, Issue , 2013, Pages 1-10

  Garcia Nieto P J ^a   Alonso Fernandez J R ^b   De Cos Juez, F J ^a   Sanchez Lasheras F ^a   Diaz Muniz C ^b  

^a UNIVERSITY OF OVIEDO   (Spain)

^b Ministerio de Agricultura Alimentación y Medio Ambiente   (Spain)

Author keywords

Cyanobacteria; Cyanotoxins; Genetic algorithms (GAs); Statistical machine learning techniques; Support vector regression (SVR)

Indexed keywords

BACTERIAL TOXIN; BACTERIAL VECTOR; CYANOTOXIN; UNCLASSIFIED DRUG;

CYANOBACTERIUM; DRINKING WATER; ENVIRONMENTAL RESEARCH; FORECASTING METHOD; GENETIC ALGORITHM; HEALTH RISK; NUMERICAL MODEL; PHYSICOCHEMICAL PROPERTY; TOXIC ORGANISM; TOXIN;

ARTICLE; CYANOBACTERIUM; GENETIC ALGORITHM; HYBRIDIZATION; NONHUMAN; PHYSICAL CHEMISTRY; PRIORITY JOURNAL; SPAIN; SUPPORT VECTOR MACHINE; TOXIN ANALYSIS;

BACTERIAL TOXINS; CYANOBACTERIA; FORECASTING; MARINE TOXINS; MICROCYSTINS; REGRESSION ANALYSIS; SPAIN; SUPPORT VECTOR MACHINES; WATER MICROBIOLOGY; WATER SUPPLY;

ASTURIAS; SPAIN; TRASONA RESERVOIR;

CYANOBACTERIA;

EID: 84875075241     PISSN: 00139351     EISSN: 10960953     Source Type: Journal    
DOI: 10.1016/j.envres.2013.01.001     Document Type: Article

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