New Pattern of Halil River Basin's Rainfall-Runoff by Using of Hybrid Neural Wavelet Network Model

Flood management and flood prediction are taken into account for a long time. There are many methods to estimate the magnitude of the flood. One of the most recent methods is using Artificial Neural Networks. In this paper, a Neural-Wavelet Network (NWN) using wavelet theory and Artificial Neural Networks (ANN) for Halil river basin in SE of Iran is reported. Furthermore, a new rainfall-runoff pattern is reported. This pattern ncludes the classification of data in the harmonic data groups and the use of Neural-Wavelet Network.The introduced pattern is analyzed by the NWN model and results are prepared with Artificial Neural Networks Back propagation and Radial Base Function (RBF) model results. Halil basin was selected to used for our model. The calculations of the R-square and root mean square error (RMSE) can control the accuracy of our computations. The calculations showed that the accuracy of the results of the Artificial Neural Wavelet Network is more desirable than ANN and RBF. It is showed that the classification of data in harmonies data and using of new pattern increase the efficiency of the models operations as well.