Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering\nhigh-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in\nthe field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used\nby hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with\nartificial neural network and finally was compared with adaptive neurofuzzy systemto predict the precipitation in Verayneh station,\nNahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series.\nThen, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with\nresults of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better\nperformance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations.
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