An improved gene expression programming model for streamflow forecasting in intermittent streams

•A new hybrid model, called GEP-GA, was proposed for streamflow forecasting in intermittent streams.•The coefficients of the best GEP expressions were optimized through GA.•The GEP-GA was superior to GEP, GP, MLR and GEP-LR models.•The GA improved GEP accuracy up to 30% in the current study. Skilful...

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Vydáno v:Journal of hydrology (Amsterdam) Ročník 563; s. 669 - 678
Hlavní autor: Danandeh Mehr, Ali
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.08.2018
Témata:
algorithms
autocorrelation
ephemeral streams
Evolutionary optimization
gene expression
Gene expression programming
Genetic algorithm
Intermittent streams
Iran
regression analysis
rivers
stream flow
Streamflow forecasting
trees
Iran
Intermittent streams
Streamflow forecasting
Genetic algorithm
Evolutionary optimization
Gene expression programming
ISSN:0022-1694, 1879-2707
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Shrnutí:•A new hybrid model, called GEP-GA, was proposed for streamflow forecasting in intermittent streams.•The coefficients of the best GEP expressions were optimized through GA.•The GEP-GA was superior to GEP, GP, MLR and GEP-LR models.•The GA improved GEP accuracy up to 30% in the current study. Skilful forecasting of monthly streamflow in intermittent rivers is a challenging task in stochastic hydrology. In this study, genetic algorithm (GA) was combined with gene expression programming (GEP) as a new hybrid model for month ahead streamflow forecasting in an intermittent stream. The hybrid model was named GEP-GA in which sub-expression trees of the best evolved GEP model were rescaled by appropriate weighting coefficients through the use of GA optimizer. Auto-correlation and partial auto-correlation functions of the streamflow records as well as evolutionary search of GEP were used to identify the optimum predictors (i.e., number of lags) for the model. The proposed methodology was demonstrated using monthly streamflow data from the Shavir Creek in Iran. Performance of the GEP-GA was compared to that of classic genetic programming (GP), GEP, multiple linear regression and GEP-linear regression models developed in the present study as the benchmarks. The results showed that the GEP-GA outperforms all the benchmarks and motivated to be used in practice.
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2018.06.049