Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks

Forecasts using the ANN for sites in 3 geographically distinct regions within Queensland are shown to be superior, with lower Root Mean Square Errors (RMSE), Mean Absolute Error (MAE) and Correlation Coefficients (r) compared to forecasts from the Predictive Ocean Atmosphere Model for Australia (POAMA), which is the General Circulation Model currently used to produce the official seasonal rainfall forecasts.