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Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting
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DOI：10.1016/j.eswa.2008.05.024.png)
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Abstract
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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 CA 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. (c) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Genetic algorithm
Neural network
Rainfall-runoff
Catchment
Semi-arid climate
Back propagation
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