Potential of Support-Vector Regression for Forecasting Stream Flow
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Date
2014
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Journal ISSN
Volume Title
Publisher
Univ Osijek, Tech Fac
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Abstract
Stream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River's daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984-January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream's flow.
Description
Akib, Shatirah/0000-0002-6538-0716; Mat Kiah, Miss Laiha/0000-0002-1240-5406; Misra, Sanjay/0000-0002-3556-9331
Keywords
stream's flow, support vector machine, neuro-fuzzy, neural networks, forecast
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Citation
WoS Q
Q4
Scopus Q
Q3
Source
Tehnicki Vjesnik
Volume
21
Issue
5
Start Page
1017
End Page
1024