A Method to Predict Heavy Precipitation using the Artificial Neural Networks with an Application


Sulaiman Junaida, Hideo Hirose


7th International Conference on Computing and Convergence Technology (ICCIT2012), pp.687-691, December 3-5, 2012, Seoul, Republic of Korea


Many flood occurrences are associated with heavy precipitation events. By using Artificial Neural Network (ANN) in predicting heavy precipitation, we can make projections of such events in the future. Since last decade, ANN applications in hydrology have grown extensively. The choice of input variables becomes crucial in identifying the optimal ANN model. This paper describes the method to predict the heavy precipitation by using ANN coupled with input variable selection (IVS) method to identify the significant inputs for heavy precipitation prediction in Malaysia. A stepwise regression method is used to find significant inputs which influences the heavy precipitation in the first phase. In the second phase, several models of ANN are built using different input sets including those which are selected during IVS process. The results of experiment revealed that the stepwise regression method increases the prediction performance of the ANN models such that they provide the useful information on the relationship between the heavy precipitation and other potential input variables

Key Words
Artificial neural network; heavy precipitation events; stepwise regression; climate indices



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