Performance of an artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a tropical site

Performance of a feed-forward back-propagation artificial neural network on forecasting the daily occurrence and annual depth of rainfall at a single meteorological station is presented. Both short-term and long-term forecasting was attempted, with ground level data collected by the meteorological station in Colombo, Sri Lanka (79 degrees 52 ' E, 6 degrees 54 ' N) during two time periods, 1994-2003 and 1869-2003. Two neural network models were developed; a one-day-ahead model for predicting the rainfall occurrence of the next day, which was able to make predictions with a 74.3% accuracy, and one-year-ahead model for yearly rainfall depth predictions with an 80-0% accuracy within a +/- 5% error bound. Each of these models was extended to make predictions several time steps into the future, where accuracies were found to decrease rapidly with the number of time steps. The success rates and rainfall variability within the north-east and south-west monsoon seasons are also discussed. Copyright (C) 2007 John Wiley & Sons. Ltd.