Predictive modelling of the stage–discharge relationship using Gene-Expression Programming

Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional method...

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Autores principales:	Prashant Birbal, Hazi Azamathulla, Lee Leon, Vikram Kumar, Jerome Hosein
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	gene-expression programming low flows modelling stage–discharge Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/12eadfa19c8a48aca1f6fa31d461657c
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id	oai:doaj.org-article:12eadfa19c8a48aca1f6fa31d461657c
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spelling	oai:doaj.org-article:12eadfa19c8a48aca1f6fa31d461657c2021-11-23T18:55:59ZPredictive modelling of the stage–discharge relationship using Gene-Expression Programming1606-97491607-079810.2166/ws.2021.111https://doaj.org/article/12eadfa19c8a48aca1f6fa31d461657c2021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3503https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional methods are available. This paper presents the Gene-Expression Programming (GEP) modelling technique to accurately model the stage–discharge relationship for the Arouca River in Trinidad and Tobago using only low flow data. The proposed method uses the stage and associated discharge measurements at one cross-section of the Arouca River. These measurements were used to train the GEP model. The results of the GEP model were also compared to the traditional method of the Stage–Discharge Rating Curve (SRC). Four statistical paraments namely the Pearson's Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and Nash–Sutcliffe Efficiency (NSE) were used to evaluate the performance of the GEP model and the SRC method. Overall, the GEP model performed exceptionally well with an R2 of 0.990, RMSE of 0.104, MARE of 0.076 and NSE of 0.957. HIGHLIGHTS The stage–discharge relationship for the Arouca River in Trinidad and Tobago was modelled using GEP via the GeneXPro software, using only low flow data.; The stage–discharge relationship for the Arouca River in Trinidad and Tobago was modelled with the SRC method using only low flow data.; The performance GEP and SRC techniques were analysed using previous research and statistical parameters such as R2, RSME, MARE and NSE.;Prashant BirbalHazi AzamathullaLee LeonVikram KumarJerome HoseinIWA Publishingarticlegene-expression programminglow flowsmodellingstage–dischargeWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3503-3514 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gene-expression programming low flows modelling stage–discharge Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	gene-expression programming low flows modelling stage–discharge Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Prashant Birbal Hazi Azamathulla Lee Leon Vikram Kumar Jerome Hosein Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
description	Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional methods are available. This paper presents the Gene-Expression Programming (GEP) modelling technique to accurately model the stage–discharge relationship for the Arouca River in Trinidad and Tobago using only low flow data. The proposed method uses the stage and associated discharge measurements at one cross-section of the Arouca River. These measurements were used to train the GEP model. The results of the GEP model were also compared to the traditional method of the Stage–Discharge Rating Curve (SRC). Four statistical paraments namely the Pearson's Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and Nash–Sutcliffe Efficiency (NSE) were used to evaluate the performance of the GEP model and the SRC method. Overall, the GEP model performed exceptionally well with an R2 of 0.990, RMSE of 0.104, MARE of 0.076 and NSE of 0.957. HIGHLIGHTS The stage–discharge relationship for the Arouca River in Trinidad and Tobago was modelled using GEP via the GeneXPro software, using only low flow data.; The stage–discharge relationship for the Arouca River in Trinidad and Tobago was modelled with the SRC method using only low flow data.; The performance GEP and SRC techniques were analysed using previous research and statistical parameters such as R2, RSME, MARE and NSE.;
format	article
author	Prashant Birbal Hazi Azamathulla Lee Leon Vikram Kumar Jerome Hosein
author_facet	Prashant Birbal Hazi Azamathulla Lee Leon Vikram Kumar Jerome Hosein
author_sort	Prashant Birbal
title	Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
title_short	Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
title_full	Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
title_fullStr	Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
title_full_unstemmed	Predictive modelling of the stage–discharge relationship using Gene-Expression Programming
title_sort	predictive modelling of the stage–discharge relationship using gene-expression programming
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/12eadfa19c8a48aca1f6fa31d461657c
work_keys_str_mv	AT prashantbirbal predictivemodellingofthestagedischargerelationshipusinggeneexpressionprogramming AT haziazamathulla predictivemodellingofthestagedischargerelationshipusinggeneexpressionprogramming AT leeleon predictivemodellingofthestagedischargerelationshipusinggeneexpressionprogramming AT vikramkumar predictivemodellingofthestagedischargerelationshipusinggeneexpressionprogramming AT jeromehosein predictivemodellingofthestagedischargerelationshipusinggeneexpressionprogramming
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Predictive modelling of the stage–discharge relationship using Gene-Expression Programming

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