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

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.