Application of artificial neural networks in flow discharge prediction for the Fitzroy River, Australia

Abstract

Prediction of flow discharge, and in particular floods, in rivers is one of the basic and key information in regards to operation and management of the river systems. The Fitzroy River, one of the largest Australian river systems, has a historical recording of heavy floods and there is a concern for the people of that area to have a clear prediction of the stream discharge to avoid damages. In this paper a feed-forward artificial neural network (ANN) model has been used to forecast the daily flow discharge of the Fitzroy River up to four days ahead. The feed-forward neural network uses error Back propagation learning algorithm. A cross validation method is applied to prevent the over-fitting problem. The network uses multiple inputs including the daily values of discharge. The network output consists of four neurons in respect to the number of forecasted days. A suitable number of inputs for time-series data were selected by trial and error. Two different multi-layer networks were compared to find the optimised network. The results show an accurate forecasting of flow discharge during flood events. However, the neural network overestimates during low discharge with a mean value of 80 (m3/s).