Prediction of precipitation applying Wavelet and ANN Model

Prediction of Precipitation is very important. Regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. Wavelet is one of the novel and very effective methods in time series and signals analyzing, that has been considered in the field of hydrology in recent years. In this research, precipitation signal has been decomposed via selected mother wavelet, and then the resulted data are used by fitting direct equations and nero-wavelet hybrid in order to anticipate the precipitation. The mentioned method was applied in Zarringol station (Iran) to predict monthly precipitation since 1975-76 until 2007-2008 for the period of 33 years. The results showed that, by decomposing signal via wavelet, the correlation among observed and calculated data were significantly increased, and the precision of prediction was improved. So that in direct method the value of R2 is equal to 0.74 and in nero-wavelet hybrid in the best case and for 4 level decomposition the value is equal to 0.95. This shows the capability of wavelet in simplifying of signal and intensification of accuracy random data in prediction of precipitation. Moreover, the meaningless F test, verifies the mentioned object.