Modeling of Rainfall by Combining Neural Computation and Wavelet Technique

Abstract The objective of this study is to develop the hybrid models by combining neural computation, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet technique for rainfall modeling. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program (IHP) representative catchment, Republic of Korea. Results obtained from this study indicate that the combination of neural computing and wavelet technique can be a useful tool for modeling of rainfall satisfactorily and can yield better efficiency than neural computing.