FLOOD FORECASTING MODEL FOR HUAI RIVER IN CHINA USING TIME DELAY NEURAL NETWORK

摘要

Time delay neural network, which is time lagged feed-forward network with delayed memory processing elements at the input layer, is applied to predict the discharge at Wangjiaba station, which is a reference station for the control of a important flood detention basin in Huai River in China. The network topology is using multiple inputs, which includes the time lagged discharges at upstream of the main trunk of the river and tributaries as input to the network, and a single output, which is the discharge at Wangjiaba station. Different types of input representations, such as the measured discharge, modified discharges, and the rate of changes in discharges have been considered by pre-processing the data. It was found that using multiple input with modified changes in discharge give the best result for prediction horizon of 12 hours. Moreover, including precipitation as input helped to improve the prediction for a longer (24 hours) prediction horizon.