Potential of Support-Vector Regression for Forecasting Stream Flow

No Thumbnail Available

Date

2014

Journal Title

Journal ISSN

Volume Title

Publisher

Univ Osijek, Tech Fac

Open Access Color

OpenAIRE Downloads

OpenAIRE Views

Research Projects

Organizational Units

Organizational Unit
Computer Engineering
(1998)
The Atılım University Department of Computer Engineering was founded in 1998. The department curriculum is prepared in a way that meets the demands for knowledge and skills after graduation, and is subject to periodical reviews and updates in line with international standards. Our Department offers education in many fields of expertise, such as software development, hardware systems, data structures, computer networks, artificial intelligence, machine learning, image processing, natural language processing, object based design, information security, and cloud computing. The education offered by our department is based on practical approaches, with modern laboratories, projects and internship programs. The undergraduate program at our department was accredited in 2014 by the Association of Evaluation and Accreditation of Engineering Programs (MÜDEK) and was granted the label EUR-ACE, valid through Europe. In addition to the undergraduate program, our department offers thesis or non-thesis graduate degree programs (MS).

Journal Issue

Events

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

Turkish CoHE Thesis Center URL

Fields of Science

Citation

WoS Q

Q4

Scopus Q

Q3

Source

Tehnicki Vjesnik

Volume

21

Issue

5

Start Page

1017

End Page

1024

Collections