this paper to use a cross-validation scheme to accurately describe predictive performance of neural networks. Cross-validation is a resampling technique which uses multiple random training and test subsamples. The advantage of cross-validation is that all of the observations or patterns in the available sample are used for testing and most of them are also used for training the model. The cross-validation analysis will yield valuable insights into the reliability of the neural networks with respect to sampling variation