Please use this identifier to cite or link to this item:
|Title:||Rainfall intensity prediction by a spatial-temporal ensemble||Authors:||Tan, T.Z.
|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. https://doi.org/10.1109/IJCNN.2008.4634030||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/115473||ISBN:||9781424418213||DOI:||10.1109/IJCNN.2008.4634030|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 21, 2020
checked on Sep 19, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.