Performance of neural networks in forecasting daily precipitation using multiple sources

Abstract

The effectiveness of neural network based models in forecasting daily precipitation, based on ground level measurements obtained from a cluster of weather stations in the dry zone of Sri Lanka, is presented. The implemented networks were based on a feed-forward back-propagation technique. A cluster of ten neighbouring weather stations having 30 years of daily precipitation data (1970-1999) was used in training and testing the models. Twenty years of daily precipitation data were used to train the networks while ten years of daily precipitation data were used to test the effectiveness of the models. One model was developed to forecast the precipitation occurrences such as 'rain' or 'no rain', while another model was developed to predict the amount of precipitation at several sub levels using fuzzy techniques. Overall, the models were able to predict the occurrence of daily precipitation with an accuracy of 79±3%. Only the nearest neighbours contributed to improving the accuracy of predictions. In the dry zone, the accuracy of predicting the dry days was superior compared to predicting wet days except during the rainy season. Fuzzy classification produced a higher accuracy in predicting 'trace' precipitation than other categories.