Artificial Neural Network (ANN)-Based Long-Term Streamflow Forecasting Models Using Climate Indices for Three Tributaries of Goulburn River, Australia

Abstract

Water resources systems planning, and control are significantly influenced by streamflow forecasting. The streamflow in northern and north-central regions of Victoria (Australia) is influenced by different climate indices, such as El Niño Southern Oscillation, Interdecadal Pacific Oscillation, Pacific Decadal Oscillation, and Indian Ocean Dipole. This paper presents the development of the ANN model using machine learning with the multi-layer perceptron and Levenberg algorithm for long-term streamflow forecasting for three tributaries of Goulburn River located within Victoria through establishing relationships between climate indices and streamflow. The climate indices were used as input predictors and the models’ performances were analyzed through best fit correlation. The higher correlation values of the developed models evident from Pearson regression (R) values ranging from 0.61 to 0.95 reveal the models’ acceptability. The accuracies of ANN models were evaluated using statistical measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). It is found that considering R, RMSE, MAE and MAPE values, the ENSO has more influence (61% to 95%) on the streamflow of Goulburn River tributaries than other climate drivers. Moreover, it is concluded that Acheron ANN models are the best models that can be confidently used to forecast the streamflow even six-months ahead.