Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic

Expert Systems with Applications

Volumn 37, Issue 8, 2010, Pages 5856-5862

  Noori, Roohollah ^a,b   Khakpour, Amir ^b,c   Omidvar, Babak ^b   Farokhnia, Ashkan ^a,d  

^a AEROSPACE RESEARCH INSTITUTE   (Iran)

^b UNIVERSITY OF TEHRAN   (Iran)

^c Laboratory and Consulting Engineering (CELCO) Company   (Iran)

^d GRADUATE UNIVERSITY OF ADVANCED TECHNOLOGY   (Iran)

Author keywords

Artificial neural networks; Developed discrepancy ratio statistic; Monthly flow; Multivariate linear regression

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; ERROR DISTRIBUTIONS; MONTHLY FLOW; MULTIVARIATE LINEAR REGRESSION MODEL; MULTIVARIATE LINEAR REGRESSIONS; RIVER FLOW; TRAINING FUNCTION; WATER RESOURCES MANAGEMENT;

FORECASTING; HYDRAULICS; LINEAR REGRESSION; MULTIVARIANT ANALYSIS; NEURAL NETWORKS; RESEARCH; STATISTICS; STREAM FLOW; WATER RESOURCES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77951204309     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.02.020     Document Type: Article

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