Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Environmental Monitoring and Assessment

Volumn 186, Issue 5, 2014, Pages 2749-2765

  Singh, Kunwar P ^a,b   Gupta, Shikha ^a,b   Rai, Premanjali ^a,b  

^a ACADEMY OF SCIENTIFIC AND INNOVATIVE RESEARCH ACSIR   (India)

^b CSIR INDIAN INSTITUTE OF TOXICOLOGY RESEARCH   (India)

Author keywords

Kernel partial least squares regression; Kernel principal component regression; Kernel ridge regression; Nonlinearity; Support vector regression

Indexed keywords

LEAST SQUARES APPROXIMATIONS; METADATA; PRINCIPAL COMPONENT ANALYSIS;

KERNEL PARTIAL LEAST SQUARES; KERNEL PRINCIPAL COMPONENT; KERNEL RIDGE REGRESSIONS; NONLINEARITY; SUPPORT VECTOR REGRESSION (SVR);

REGRESSION ANALYSIS;

CONCENTRATION (COMPOSITION); DATA SET; DISSOLVED OXYGEN; LEAST SQUARES METHOD; NONLINEARITY; PREDICTION; REGRESSION ANALYSIS; SURFACE WATER; WATER CHEMISTRY; WATER QUALITY;

ARTICLE; CONCENTRATION (PARAMETERS); KERNEL METHOD; KERNEL PARTIAL LEAST SQUARES REGRESSION; KERNEL PRINCIPAL COMPONENT REGRESSION; KERNEL RIDGE REGRESSION; NONLINEAR SYSTEM; OXYGEN CONCENTRATION; PREDICTION; PROCESS OPTIMIZATION; SUPPORT VECTOR MACHINE; SURFACE WATER HYDROLOGY; SYSTEM ANALYSIS; VALIDATION STUDY;

ALGORITHMS; BIOLOGICAL OXYGEN DEMAND ANALYSIS; BIOMETRY; ENVIRONMENTAL MONITORING; FRESH WATER; LEAST-SQUARES ANALYSIS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; OXYGEN; REGRESSION ANALYSIS;

EID: 84898902434     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-013-3576-6     Document Type: Article

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