The Interval Estimation of Parameters for Neural Network on Flood Forecasting

The nonlinearity and uncertainty of the flood process are such that estimating or predicting required hydrologic data is often tremendous difficult. Consequently, this study employs a Back-Propagation Network (BPN) as the main structure in flood forecasting to learn and demonstrate the sophisticated nonlinear mapping relationship. However, sophisticated natural systems and highly changeable hydrological environments require that the construction of an artificial neural network (ANN) as a forecasting model should include a risk analysis to reflect the hydrological situation or/and physical meaning of the predicted results. In this paper, a Self Organizing Map (SOM) network with classification ability was applied to the solutions and parameters of BPN model in the learning stage, to classify the network parameter rules and obtain the winning parameters. Hence, hydrologic data intervals can then be forecasted, with the outcomes from the previous stage used as the ranges of the parameters in the recall stage. Overall, this research develops a methodology for providing the decision-maker with more flexibility in forecasting floods.