Please use this identifier to cite or link to this item:
|Title:||Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory||Authors:||Sun, Y.
Model error prediction
Time-delay neural networks
|Issue Date:||6-Dec-2010||Citation:||Sun, Y., Babovic, V., Chan, E.S. (2010-12-06). Multi-step-ahead model error prediction using time-delay neural networks combined with chaos theory. Journal of Hydrology 395 (1-2) : 109-116. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jhydrol.2010.10.020||Abstract:||This paper presents a time series prediction scheme using time-delay neural networks combined with chaos theory. To achieve reliable multi-step-ahead prediction, the optimal architecture of networks is determined by average mutual information and false nearest neighbors analyses in chaos theory. The networks are applied to predict the model errors at four measurement stations in the Singapore Regional Model domain, with five prediction horizons ranging from 2 h to 96 h. It is found that the combined scheme significantly improves the accuracy of tidal prediction, with more than 70% of the root mean square errors removed for 2 h tidal forecast and more than 50% for 96 h tidal forecast. © 2010 Elsevier B.V.||Source Title:||Journal of Hydrology||URI:||http://scholarbank.nus.edu.sg/handle/10635/65853||ISSN:||00221694||DOI:||10.1016/j.jhydrol.2010.10.020|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.