Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Adnan, RM; Yuan, X; Kisi, O; Anam, R

Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm

Adnan, RM Yuan, X Kisi, O Anam, R

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Publisher:: Hindawi Limited
Publication Type:: Journal Article
Citation:: Advances in Meteorology, 2017, 2017, pp. 1-23
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Adnan, RM
dc.contributor.author	Yuan, X
dc.contributor.author	Kisi, O
dc.contributor.author	Anam, R
dc.date.accessioned	2022-03-20T23:52:48Z
dc.date.available	2022-03-20T23:52:48Z
dc.date.issued	2017-01-01
dc.identifier.citation	Advances in Meteorology, 2017, 2017, pp. 1-23
dc.identifier.issn	1687-9309
dc.identifier.issn	1687-9317
dc.identifier.uri	http://hdl.handle.net/10453/155388
dc.description.abstract	<jats:p>River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.</jats:p>
dc.language	en
dc.publisher	Hindawi Limited
dc.relation.ispartof	Advances in Meteorology
dc.relation.isbasedon	10.1155/2017/2391621
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0401 Atmospheric Sciences
dc.title	Improving Accuracy of River Flow Forecasting Using LSSVR with Gravitational Search Algorithm
dc.type	Journal Article
utslib.citation.volume	2017
utslib.for	0401 Atmospheric Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.date.updated	2022-03-20T23:52:46Z
pubs.publication-status	Published
pubs.volume	2017

Abstract:

River flow prediction is essential in many applications of water resources planning and management. In this paper, the accuracy of multivariate adaptive regression splines (MARS), model 5 regression tree (M5RT), and conventional multiple linear regression (CMLR) is compared with a hybrid least square support vector regression-gravitational search algorithm (HLGSA) in predicting monthly river flows. In the first part of the study, all three regression methods were compared with each other in predicting river flows of each basin. It was found that the HLGSA method performed better than the MARS, M5RT, and CMLR in river flow prediction. The effect of log transformation on prediction accuracy of the regression methods was also examined in the second part of the study. Log transformation of the river flow data significantly increased the prediction accuracy of all regression methods. It was also found that log HLGSA (LHLSGA) performed better than the other regression methods. In the third part of the study, the accuracy of the LHLGSA and HLGSA methods was examined in river flow estimation using nearby river flow data. On the basis of results of all applications, it was found that LHLGSA and HLGSA could be successfully used in prediction and estimation of river flow.

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http://hdl.handle.net/10453/155388