Real-time hydrological forecasting method of artificial neural network based on forgetting factor

Flood system is usually very complex,and always changes with different inflow from upstream,local rainfall,riverbed deformation and other factors.When the back propagation (BP) neural network is applied in such system for flood forecasting,the algorithm must have ability for real-time tracing of the changes of parameters in the system.In this paper,a variable weighted forgetting factor based on recursive least-squares parameter estimation is introduced into the BP model to simulate such time-variant system.Each weight of the neural network can be real-time modified and the transitional invariable mapping relationship between input and output in the non-liner system of neural network is improved.And two examples are given to demonstrate the effectiveness of the improvement.The calculated result shows that the time-variable weights can be traced with a fast speed and agrees well with the measured data.