shows the comparison between predicted and measured
runoff values at training and testing phases by hybrid GA–ANN
model using the daily data from the Ourika catchment. The GA–
ANN algorithm was run with a population size of 100, uniform
crossover probability was set to 0.9 and uniform mutation probability
was set to 0.1. GA–ANN was trained by 80 generations, followed
by a BP training procedure. The value of learning
coefficient 0.01 and momentum correction factor 0.08 were used
for the back-propagation training algorithm.
In Fig. 3 the output of the model, simulated with test data,
shows a good agreement with the target. The simulation performance
of the GA–ANN model was evaluated on the basis of root
mean square error (RMSE) and efficiency coefficient R2 (Nash &
Sutcliffe, 1970). The parameters RMSE = 0.162 and R2 = 0.91 suggest
a very good performance. In general, a R2 value greater than
0.9 indicates a very satisfactory model performance, while a R2 value
in the range 0.8–0.9 signifies a good performance and values
less than 0.8 indicate an unsatisfactory model performance (Coulibaly
& Baldwin, 2005).
In order to evaluate the performance of the genetic algorithmbased
neural network, back-propagation neural network was applied
with the same data sets used in the GA–ANN model. Fig. 4
shows the extent of the match between the measured and predicted
daily flow values by GA–BP and BP neural networks in term