Abstract: To effectively forecast estuarine daily river stage, which is influenced by complicated
environmental factors because of estuarine special geographic location, discrete wavelet transform (DWT)
and neuro-fuzzy algorithm (NF) were combined to construct a hybrid DWT-NF model for mid-long term
forecast of daily river stage in the Yantze estuary. This hybrid model uses DWT for decomposion of the
original stage signals and filtering its jamming noises out. DWT revealed an optimum combination of
decomposition factors TD(D3+D4+D8), so we selected different TD serial combinations at a lag of 1 day to 5
days as inputs of the NF sub-model, and constructed an optimum WT-NF model for estuarine daily stage by
training and testing different NF model structures. In forecasting the daily river stage in Santiao Port, the
best NF model inputs with three nodes of TD serials at 1 day to 3 days lag were transferred into the first
model layer using a Gauss function of rule number 43; in Qinglong Port, the best inputs with TD serials at 1
day to 4 days lag transferred using a Bell function of rule number 24. Comparison with other hybrid or
traditional models shows that the hybrid DWT-NF model has a much better performance, especially
prominent in effectively forecasting detailed fluctuation trends of estuarine daily river stage dynamics.