Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting

Authors:
A. Sedki;D. Ouazar;E. El Mazoudi
Affiliations:
Department of Civil Engineering, Ecole Mohammadia d'Ingènieurs, Universitè Mohammed V-Agdal, 765, Agdal, Rabat, Morocco;Department of Civil Engineering, Ecole Mohammadia d'Ingènieurs, Universitè Mohammed V-Agdal, 765, Agdal, Rabat, Morocco;Department of Electrical Engineering, Faculty of Sciences and Technique, Fès-Sais, Morocco
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network for rainfall-runoff forecasting and its application to predict the runoff in a catchment located in a semi-arid climate in Morocco. To predict the runoff at given moment, the input variables are the rainfall and the runoff values observed on the previous time period. Our methodology adopts a real coded GA strategy and hybrid with a back-propagation (BP) algorithm. The genetic operators are carefully designed to optimize the neural network, avoiding premature convergence and permutation problems. To evaluate the performance of the genetic algorithm-based neural network, BP neural network is also involved for a comparison purpose. The results showed that the GA-based neural network model gives superior predictions. The well-trained neural network can be used as a useful tool for runoff forecasting.