Abstract
Generalized regression neural network (GRNN) is commonly used for function approximation. The predictability of GRNN for Nile flood forecasting is investigated through optimization of the smoothing parameter using fruit-fly (FOA) algorithm. Due to the excess number of inputs that are highly correlated, principal component analysis (PCA) is used to reduce the dimension of a set of linearly correlated variables to improve forecasting task. Our empirical experiment shows that the performance of GRNN is better than other neural network included in this paper. The results of the prediction made by the proposed model showed a close fit with the actual data.
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