Abstract
AbstractThe ability of wavelet transform (WT) to simultaneously deal with both the spectral and temporal information contained within time series data makes it popular to use in modeling the rainfall-runoff process over a catchment. This study explores the potential of hybrid Wavelet Co-Active Neuro Fuzzy Inference System (WCANFIS) models for simulating the transformation of rainfall-runoff process in the Baihe catchment located in China. The study investigates the selection of suitable settings for wavelet-based neuro-fuzzy rainfall-runoff models. These settings include the choice of a suitable wavelet function and the number of decomposition levels to be employed. For the development of wavelet neuro-fuzzy rainfall-runoff models, the input rainfall data is transformed by using the Discrete Wavelet Transformation (DWT). Ten different wavelet functions including the simple mother wavelet Haar; db2, db4, and db8 wavelet functions from the most popular wavelet family Daubechies; the Sym2, Sym4, Sym8 wavelet...
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