Improving streamflow forecast using optimal rain gauge network-based input to artificial neural network models

Abstract

Abstract Accurate streamflow forecasting is of great importance for the effective management of water resources systems. In this study, an improved streamflow forecasting approach using the optimal rain gauge network-based input to artificial neural network (ANN) models is proposed and demonstrated through a case study (the Middle Yarra River catchment in Victoria, Australia). First, the optimal rain gauge network is established based on the current rain gauge network in the catchment. Rainfall data from the optimal and current rain gauge networks together with streamflow observations are used as the input to train the ANN. Then, the best subset of significant input variables relating to streamflow at the catchment outlet is identified by the trained ANN. Finally, one-day-ahead streamflow forecasting is carried out using ANN models formulated based on the selected input variables for each rain gauge network. The results indicate that the optimal rain gauge network-based input to ANN models gives the best streamflow forecasting results for the training, validation and testing phases in terms of various performance evaluation measures. Overall, the study concludes that the proposed approach is highly effective to achieve the enhanced streamflow forecasting and could be a viable option for streamflow forecasting in other catchments.