Integration of GWO-LSSVM for time series predictive analysis

The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. In this study, LSSVM is employed as a predictor to predict water level. Accurate water levels model...

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Main Authors:	Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Bariah, Yusob, Ernawan, Ferda
Format:	Conference or Workshop Item
Language:	English
Published:	IET 2016
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/1/Integration%20Of%20GWO-LSSVM%20For%20Time%20Series%20Predictive%20Analysis.pdf

id	ump-21490
recordtype	eprints
spelling	ump-214902018-09-12T07:29:03Z http://umpir.ump.edu.my/id/eprint/21490/ Integration of GWO-LSSVM for time series predictive analysis Zuriani, Mustaffa Mohd Herwan, Sulaiman Bariah, Yusob Ernawan, Ferda QA75 Electronic computers. Computer science The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. In this study, LSSVM is employed as a predictor to predict water level. Accurate water levels modeling and prediction is vital for safety especially during the monsoon season. It is worth noting that, mitigating the effects of floods can be accomplished by using either structural or non-structural measures, or by a combination of both. Structural measures incorporated of engineering works, such as channelization or flood reservoirs which changes the shape of the flood hydrograph. On the other hand, non-structural measures such as flood warning scheme is intended to reduce the economic losses in a flood situation. In most cases, disaster prevention operation such flood warning scheme proved to be more efficient in mitigating the effects of major floods than structural measures. Thus, for this study, a hybrid algorithm of LSSVM with one of the recent bio-inspired optimization algorithm, namely Grey Wolf Optimizer (GWO-LSSVM) is presented for water level prediction. The GWO is utilized to optimize the parameters of LSSVM. The feasibility of GWO-LSSVM is compared against LSSVM optimized by Firefly Algorithm (FA-LSSVM) and single GWO. Findings of the study demonstrated that the GWO-LSSVM able to provide competitive results and able to perform well for the problem under study. IET 2016 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/21490/1/Integration%20Of%20GWO-LSSVM%20For%20Time%20Series%20Predictive%20Analysis.pdf Zuriani, Mustaffa and Mohd Herwan, Sulaiman and Bariah, Yusob and Ernawan, Ferda (2016) Integration of GWO-LSSVM for time series predictive analysis. In: 4th IET Clean Energy and Technology Conference, CEAT 2016, 14-15 November 2016 , Kuala Lumpur, Malaysia. pp. 1-5., 2016 (CP688). ISBN 978-1-78561-238-1 https://ieeexplore.ieee.org/document/8278664/
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Zuriani, Mustaffa Mohd Herwan, Sulaiman Bariah, Yusob Ernawan, Ferda Integration of GWO-LSSVM for time series predictive analysis
description	The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. In this study, LSSVM is employed as a predictor to predict water level. Accurate water levels modeling and prediction is vital for safety especially during the monsoon season. It is worth noting that, mitigating the effects of floods can be accomplished by using either structural or non-structural measures, or by a combination of both. Structural measures incorporated of engineering works, such as channelization or flood reservoirs which changes the shape of the flood hydrograph. On the other hand, non-structural measures such as flood warning scheme is intended to reduce the economic losses in a flood situation. In most cases, disaster prevention operation such flood warning scheme proved to be more efficient in mitigating the effects of major floods than structural measures. Thus, for this study, a hybrid algorithm of LSSVM with one of the recent bio-inspired optimization algorithm, namely Grey Wolf Optimizer (GWO-LSSVM) is presented for water level prediction. The GWO is utilized to optimize the parameters of LSSVM. The feasibility of GWO-LSSVM is compared against LSSVM optimized by Firefly Algorithm (FA-LSSVM) and single GWO. Findings of the study demonstrated that the GWO-LSSVM able to provide competitive results and able to perform well for the problem under study.
format	Conference or Workshop Item
author	Zuriani, Mustaffa Mohd Herwan, Sulaiman Bariah, Yusob Ernawan, Ferda
author_facet	Zuriani, Mustaffa Mohd Herwan, Sulaiman Bariah, Yusob Ernawan, Ferda
author_sort	Zuriani, Mustaffa
title	Integration of GWO-LSSVM for time series predictive analysis
title_short	Integration of GWO-LSSVM for time series predictive analysis
title_full	Integration of GWO-LSSVM for time series predictive analysis
title_fullStr	Integration of GWO-LSSVM for time series predictive analysis
title_full_unstemmed	Integration of GWO-LSSVM for time series predictive analysis
title_sort	integration of gwo-lssvm for time series predictive analysis
publisher	IET
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/ http://umpir.ump.edu.my/id/eprint/21490/1/Integration%20Of%20GWO-LSSVM%20For%20Time%20Series%20Predictive%20Analysis.pdf
first_indexed	2023-09-18T22:31:34Z
last_indexed	2023-09-18T22:31:34Z
_version_	1777416320372965376

Integration of GWO-LSSVM for time series predictive analysis

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