MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction

Mehr, AD; Gandomi, AH

MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction

Mehr, AD Gandomi, AH

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Information Sciences, 2021, 561, pp. 181-195
Issue Date:: 2021-06-01

Closed Access

	Filename	Description	Size
	1-s2.0-S0020025521001456-main.pdf		3.58 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Mehr, AD
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2022-03-23T02:41:13Z
dc.date.available	2022-03-23T02:41:13Z
dc.date.issued	2021-06-01
dc.identifier.citation	Information Sciences, 2021, 561, pp. 181-195
dc.identifier.issn	0020-0255
dc.identifier.issn	1872-6291
dc.identifier.uri	http://hdl.handle.net/10453/155482
dc.description.abstract	This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-least-square models. It is explicit and promising for real-life applications.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Information Sciences
dc.relation.isbasedon	10.1016/j.ins.2021.02.011
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction
dc.type	Journal Article
utslib.citation.volume	561
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-03-23T02:41:09Z
pubs.publication-status	Published
pubs.volume	561

Abstract:

This paper presents the development and verification of a new multi-stage genetic programming (MSGP) technique, called MSGP-LASSO, which was applied for univariate streamflow forecasting in the Sedre River, an intermittent river in Turkey. The MSGP-LASSO is a practical and cost-neutral improvement over classic genetic programming (GP) that increases modelling accuracy, while decreasing its complexity by coupling the MSGP and multiple regression LASSO methods. The new model uses average mutual information to identify the optimum lags, and root mean-square technique to minimize forecasting error. Based on Nash-Sutcliffe efficiency and bias-corrected Akaike information criterion, MSGP-LASSO is superior to GP, multigene GP, MSGP, and hybrid MSGP-least-square models. It is explicit and promising for real-life applications.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/155482