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Title: Rainfall intensity prediction by a spatial-temporal ensemble
Authors: Tan, T.Z.
Lee, G.K.K.
Liong, S.-Y. 
Lim, T.K.
Chu, J.
Hung, T.
Issue Date: 2008
Citation: Tan, T.Z.,Lee, G.K.K.,Liong, S.-Y.,Lim, T.K.,Chu, J.,Hung, T. (2008). Rainfall intensity prediction by a spatial-temporal ensemble. Proceedings of the International Joint Conference on Neural Networks : 1721-1727. ScholarBank@NUS Repository.
Abstract: Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry periods in between raining seasons. Hence, conventional computational intelligence tools sometimes are not able to offer acceptable accuracy. An ensemble constitutes of classification, regression and reward models is proposed. The classification model identifies rain or no rain episodes, whereas the regression model predicts the rainfall intensity. The error of the regression model is then predicted by the reward regression model. Through that, the spatial information is captured by the classification model, and the temporal information is captured by the regression and reward models. Preliminary experimental results are encouraging. © 2008 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
ISBN: 9781424418213
DOI: 10.1109/IJCNN.2008.4634030
Appears in Collections:Staff Publications

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