Monthly rainfall forecasting using neural networks for sugarcane regions in Eastern Australia

Abstract

Sugarcane is an important agricultural crop grown on the east coast of Australia. The timing and amount of rainfall is critical in determining both the yield of sugar and scheduling of harvesting operations. Rainfall forecasts issued through the Australian Bureau of Meteorology are based on general circulation models (GCMs) and have a poor skill levels. They are also limited in utility to end-users such as farmers as they cover very broad geographical areas and are only issued as probabilities above or below median. This paper presents an alternative approach for forecasting monthly rainfall with up to 12 month lead-time based on machine learning, in particular neural networks. Monthly rainfall forecasts have been developed for the eight locations in Eastern Australia at 3 and 12 month lead-time. The accuracy of the forecasts has been assessed relative to a skill scale with 0% representing climatology (the long term average) and 100% representing a perfect forecast (observation). On this scale, neural network forecasts are typically in the range 39.9–68% for all months using a single month optimization. This compares very favorably with forecasts using GCM from the Bureau that have skill levels only in the range −20% to 20%.