Auto-configuring radial basis function networks for chaotic time series and flood forecasting

doi:10.1002/hyp.7352

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Journal Article > Item 987654321/50584

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/50584

Title:	Auto-configuring radial basis function networks for chaotic time series and flood forecasting
Authors:	張麗秋;Chang, Li-chiu;Chang, Fi-John;Wang, Yuan-peng
Contributors:	淡江大學水資源與環境工程學系
Keywords:	radial basis function network;genetic algorithm;Mackey-Glass time series;flood forecast
Date:	2009-08
Issue Date:	2010-08-09 20:40:59 (UTC+8)
Publisher:	Bognor Regis: John Wiley & Sons Ltd.
Abstract:	The learning strategy of the radial basis function network (RBFN) commonly uses a hybrid learning process to identify the structure and then proceed to search the model parameters, which is a time-consuming procedure. We proposed an evolutionary way to automatically configure the structure of RBFN and search the optimal parameters of the network. The strategy can effectively identify an appropriate structure of the network by the orthogonal least squares algorithm and then systematically search the optimal locations of centres and the widths of their corresponding kernel function by the genetic algorithm. The proposed strategy of auto-configuring RBFN is first testified in predicting the future values of the chaotic Mackey-Glass time series. The results demonstrate the superiority, on both effectiveness and efficiency, of the proposed strategy in predicting the chaotic time series. We then further investigate the model's suitability and reliability in flood forecast. The Lan-Young River in north-east Taiwan is used as a case study, where the hourly river flow of 23 flood events caused by typhoons or storms is used to train and validate the neural networks. The back propagation neural network (BPNN) is also performed for the purpose of comparison. The results demonstrate that the proposed RBFN has much better performance than the BPNN. The RBFN not only provides an efficient way to model the rainfall-runoff process but also gives reliable and precise one-hour and two-hour ahead flood forecasts.
Relation:	Hydrological Processes 23(17), pp.2450-2459
DOI:	10.1002/hyp.7352
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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