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

There have been many theoretical studies of the nature of concurrent relationships between climate indices and rainfall for Queensland, but relatively few of these studies have rigorously tested the lagged relationships (the relationships important for forecasting), particularly within a forecast model. Through the use of artificial neural networks (ANNs) we evaluate the utility of climate indices in terms of their ability to forecast rainfall as a continuous variable. Results using ANNs highlight the value of the Inter-decadal Pacific Oscillation, an index never used in the official seasonal forecasts for Queensland that, until recently, were based on statistical models. 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.