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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Bibliographic Details
Published in:Water science & technology. Water supply 2021-11, Vol.21 (7), p.3503-3514
Main Authors: Birbal, Prashant, Azamathulla, Hazi, Leon, Lee, Kumar, Vikram, Hosein, Jerome
Format: Article
Language:English
Subjects:
Correlation coefficient
Correlation coefficients
Creeks & streams
Discharge
Floods
Gene expression
gene-expression programming
Genetic engineering
Hydrology
Low flow
low flows
Methods
Modelling
Prediction models
Rivers
Root-mean-square errors
stage–discharge
Water management
Water resources
Water resources management
Water shortages
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Summary: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.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2021.111