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Paper Information

Journal:   WATER AND WASTEWATER   2011 , Volume 22 , Number 1 (77); Page(s) 118 To 123.
 
Paper: 

MONTHLY STREAM FLOW PREDICTION USING SUPPORT VECTOR MACHINE BASED ON PRINCIPAL COMPONENT ANALYSIS

 
 
Author(s):  NOORI ROOHOLLAH*, KHAKPOUR AMIR, DEHGHANI MAJID, FAROKHNIA ASHKAN
 
* FACULTY OF ENVIRONMENT, UNIVERSITY OF TEHRAN
 
Abstract: 

The main goal of this research is to evaluate the role of input selection by Principal Component Analysis (PCA) on Support Vector Machine (SVM) performance for monthly stream flow prediction. For this purpose, SVM is used to predict monthly flow as a function of 18 input variables. PCA is subsequently employed to reduce the number of input variables from 18 to 5 PCs which are finally fed into the SVM model. SVM and PCA-SVM models are evaluated in terms of their performance using a developed statistic by the authors. Findings show that preprocessing of input variables by PCA improved SVM performance.

 
Keyword(s): SUPPORT VECTOR MACHINE, PRINCIPAL COMPONENT ANALYSIS, MONTHLY STREAM FLOW
 
References: 
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