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6445284 
Journal Article 
Wavelet analysis-fuzzy neural network based runoff forecasting model 
Du, F 
2013 
44 
2 
5-8 
In accordance with the wavelet decomposition and reconstruction technology as well as the training cycles of various fuzz-y neural network models, four runoff forecast models based on the combination of wavelet analysis with fuzzy neural network, i. e. Mallat algorithm based long cycle runoff forecasting model; Mallat algorithm based short cycle runoff forecasting model; wavelet packet algorithm based long runoff forecasting model; wavelet packet algorithm based short cycle runoff forecasting model, are put forward herein, and then the principle, structure and step of the establishment of the models are expatiated as well. Moreover, by taking the monthly runoff data from Tangnaihai Hydrological Station-one of the outlet hydrological stations at the source regions of the Yellow River as the applied case, the four models mentioned above are comparatively evaluated with the cycle decomposition coefficient and Nash-Sutcliffe efficiency coefficient. The result shows that the forecasting effect is best from the Mallat algorithm based long cycle runoff forecasting model and that from the wavelet packet algorithm based short cycle runoff forecasting model is worst. Thereby, the main causation of this phenomenon is also analyzed herein. Furthermore, some reasonable suggestions on the application of both the wavelet analysis and the fuzzy neural network to hydrological model are presented as well. 
; Prediction; Mathematical models; Degradation; Wave forecasting; Wave analysis; Water resources; Runoff; Modelling; Hydrologic analysis; Neural networks; Algorithms; Rainfall-runoff modeling; Wavelet analysis; Runoff forecasting; Artificial intelligence; Training; Hydroelectric power; Decomposition; Technology; Outlets; Hydrologic Models; Hydroelectric Plants; Hydrologic Data/