Generalization performance of support vector machines and neural networks in runoff modeling

Expert Systems with Applications

Volumn 36, Issue 4, 2009, Pages 7624-7629

  Behzad, Mohsen ^a   Asghari, Keyvan ^a   Eazi, Morteza ^a   Palhang, Maziar ^a  

^a ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

Author keywords

Artificial neural networks (ANNs); Rainfall and climate data; Runoff prediction; Support vector machines (SVMs)

Indexed keywords

ARID REGIONS; BACKPROPAGATION; CONVEX OPTIMIZATION; FORECASTING; IMAGE RETRIEVAL; NEURAL NETWORKS; OPTIMIZATION; REAL TIME SYSTEMS; RUNOFF; STREAM FLOW; VECTORS; WATER RESOURCES;

ARTIFICIAL NEURAL NETWORKS (ANNS); CONVEX QUADRATIC OPTIMIZATION PROBLEMS; DATA-DRIVEN MODELS; EMPIRICAL RISK MINIMIZATIONS; FORECASTING CAPABILITIES; FORECASTING METHODS; GENERALIZATION PERFORMANCE; GENERALIZATION PROPERTIES; HYDRO-POWER SYSTEMS; LEAD STREAMS; LOCAL CLIMATES; PREDICTION ACCURACIES; RAINFALL AND CLIMATE DATA; RAINFALL DATUM; REGRESSION TECHNIQUES; ROOT MEAN SQUARED ERRORS; RUNOFF MODELING; RUNOFF PREDICTION; SITE SPECIFICS; SPARSE REPRESENTATIONS; SQUARED CORRELATION COEFFICIENTS; STRUCTURAL RISK MINIMIZATION PRINCIPLES; SUPPORT VECTOR MACHINES (SVMS); WATER RESOURCES MANAGEMENTS;

SUPPORT VECTOR MACHINES;

EID: 60249084777     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.09.053     Document Type: Article

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