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Prediction of flow duration curve in ungauged catchments using genetic expression programming

A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to sev...

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Main Authors:	Razaq, S. A., Shahid, S., Ismail, T., Chung, E. S., Mohsenipour, M., Wang, X. J.
Format:	Conference or Workshop Item
Published:	Elsevier Ltd 2016
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/73698/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997830635&doi=10.1016%2fj.proeng.2016.07.516&partnerID=40&md5=62a5654aff692b5346b5eb263db23a0d
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spelling	my.utm.736982017-11-29T23:58:42Z http://eprints.utm.my/id/eprint/73698/ Prediction of flow duration curve in ungauged catchments using genetic expression programming Razaq, S. A. Shahid, S. Ismail, T. Chung, E. S. Mohsenipour, M. Wang, X. J. TA Engineering (General). Civil engineering (General) A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to seven candidate explanatory variables prepared from climatic, geomorphologic, geographic characteristics, soil properties, and land use and land cover information. Support vector machine (SVM) was used to optimize the best combinations for calibration and validation of GEP models from the data available in thirteen gauged catchments in the study area. Seven flow percentiles namely 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95 as well as extreme, maximum, minimum and mean annual flows were identified to develop a framework for predicting various flow metrics. Obtained results revealed that nonlinear regression equations developed using GEP can generate FDCs in ungauged catchments of East Coast of Peninsular Malaysia with an efficiency of up to 0.92. Elsevier Ltd 2016 Conference or Workshop Item PeerReviewed Razaq, S. A. and Shahid, S. and Ismail, T. and Chung, E. S. and Mohsenipour, M. and Wang, X. J. (2016) Prediction of flow duration curve in ungauged catchments using genetic expression programming. In: 12th International Conference on Hydroinformatics - Smart Water for the Future, HIC 2016, 21-26 Aug 2016, South Korea. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997830635&doi=10.1016%2fj.proeng.2016.07.516&partnerID=40&md5=62a5654aff692b5346b5eb263db23a0d
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Razaq, S. A. Shahid, S. Ismail, T. Chung, E. S. Mohsenipour, M. Wang, X. J. Prediction of flow duration curve in ungauged catchments using genetic expression programming
description	A set of multivariate equations have been developed using gene expression programming (GEP) based symbolic regression technique to generate the flow quantiles of flow duration curve (FDC) in the ungauged catchments in the East Coast of Peninsular Malaysia. The equations were derived from four to seven candidate explanatory variables prepared from climatic, geomorphologic, geographic characteristics, soil properties, and land use and land cover information. Support vector machine (SVM) was used to optimize the best combinations for calibration and validation of GEP models from the data available in thirteen gauged catchments in the study area. Seven flow percentiles namely 0.05, 0.10, 0.25, 0.50, 0.75, 0.90, and 0.95 as well as extreme, maximum, minimum and mean annual flows were identified to develop a framework for predicting various flow metrics. Obtained results revealed that nonlinear regression equations developed using GEP can generate FDCs in ungauged catchments of East Coast of Peninsular Malaysia with an efficiency of up to 0.92.
format	Conference or Workshop Item
author	Razaq, S. A. Shahid, S. Ismail, T. Chung, E. S. Mohsenipour, M. Wang, X. J.
author_facet	Razaq, S. A. Shahid, S. Ismail, T. Chung, E. S. Mohsenipour, M. Wang, X. J.
author_sort	Razaq, S. A.
title	Prediction of flow duration curve in ungauged catchments using genetic expression programming
title_short	Prediction of flow duration curve in ungauged catchments using genetic expression programming
title_full	Prediction of flow duration curve in ungauged catchments using genetic expression programming
title_fullStr	Prediction of flow duration curve in ungauged catchments using genetic expression programming
title_full_unstemmed	Prediction of flow duration curve in ungauged catchments using genetic expression programming
title_sort	prediction of flow duration curve in ungauged catchments using genetic expression programming
publisher	Elsevier Ltd
publishDate	2016
url	http://eprints.utm.my/id/eprint/73698/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997830635&doi=10.1016%2fj.proeng.2016.07.516&partnerID=40&md5=62a5654aff692b5346b5eb263db23a0d
_version_	1643656723146211328
score	13.211869

Prediction of flow duration curve in ungauged catchments using genetic expression programming

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