Particle swarm optimization feedforward neural network for modeling runoff|
Kuok, K. K.; Harun, S. & Shamsuddin, S. M.
The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years,
hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological
processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets.
However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local
minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train
the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the
daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using
the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall,
antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward
neural network simulated the current runoff accurately with R = 0.872 and E2 = 0.775 for the training data set and
R = 0.900 and E2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward
neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be
to be applied to other basins.
Backpropagation neural network; Coefficient of correlation; Modeling runoff; Nash-Sutcliffe coefficient; Particle swarm optimization feedforward neural network