Dynamic Water-Level Neural-Network Forecast Model on Non-Stationary Time Series
Xue Lian-qing, Cui Guang-bai, CHEN Kaiqi · Journal of Lake Sciences · 2002
Hydrology prediction is a complex non linear dynamic process, and the station water level often shows dynamic changing character owing to all kinds of factors. In the Huaihe Basin, Wuhe station water level will be influenced by the backwater influence of Hongze lake and shows the non statinoary changing. In the paper based on the neural network model of time series and the data characteristics of hydrology, a non stationary multi station variable dynamic sequence prediction model is made by using artificial neural network and practised in Wuhe station water level prediction of Huaihe River. The calculation results indicates that the model is not only reasonable but also its predicting period is longer. It is valuable when being used in practices.