Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2009.1786.1790GholizadehM.H. DarandM. 9200999Artificial Neural Networks (ANN), which emulate the
parallel distributed processing of the human nervous system, have proven
to be very successful in dealing with complicated problems, such as function
approximation and pattern recognition. Rainfall forecasting has been a
difficult subject due to the complexity of the physical processes involved
and the variability of rainfall in space and time. Artificial Neural Networks
(ANN), which perform a nonlinear mapping between inputs and outputs, are
one of the techniques that are suitable for rainfall forecasting. Multiple
perceptron neural networks were trained with actual monthly precipitation
data from Tehran station for a time period of 53 years. Predicted amounts
are of next-month-precipitation in the next year. The ANN models provided
a good fit with the actual data and have shown a high feasibility in prediction
of month rainfall precipitation. Combination neural networks with Genetic
algorithm showed better results.]]>Fernando, D.A.K. and A.W. Jayawardena,19983203209Carlos, A., C. Coello and G.T. Pulido, 20012001pp: 126140Chakraborty, K., K. Mehrotra, K.M. Chilukuri and R. Sanjay,19925961970French, M.N., W.F. Krajewski and R.R. Cuykendall,1992137131Hornik, K., M. Stinchcombe and H. White,19892359366Hsu, K.I., H.V. Gupta and S. Sorooshian,19953125172530Karunanithi, N., W.J. Grenney, D. Whitley and K. Bovee,19948201228Erb, R.J.,199310165170Sexton, R.S., B. Alidaee, R.E. Dorsey and J.D. Johnson, 1998106570584Saad, M. and P. Bigras, 199632179186Smith, J. and R.N. Eli, 1995121499508Xiao, R. and V. Chandrasekar, 199735160171Zhu, M.L. and M. Fujita,199412131141