Enforced self-organizing map neural networks for river flood forecasting

doi:10.1002/hyp.6262

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Journal Article > Item 987654321/44551

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/44551

Title:	Enforced self-organizing map neural networks for river flood forecasting
Authors:	Chang, Fi-john;張麗秋;Chang, Li-chiu;Wang, Yan-shiang
Contributors:	淡江大學水資源及環境工程學系
Date:	2007-03-01
Issue Date:	2010-03-26 16:17:20 (UTC+8)
Publisher:	Wiley-Blackwell
Abstract:	Self-organizing maps (SOMs) have been successfully accepted widely in science and engineering problems; not only are their results unbiased, but they can also be visualized. In this study, we propose an enforced SOM (ESOM) coupled with a linear regression output layer for flood forecasting. The ESOM re-executes a few extra training patterns, e.g. the peak flow, as recycling input data increases the mapping space of peak flow in the topological structure of SOM, and the weighted sum of the extended output layer of the network improves the accuracy of forecasting peak flow. We have investigated an ESOM neural network by using the flood data of the Da-Chia River, Taiwan, and evaluated its performance based on the results obtained from a commonly used back-propagation neural network. The results demonstrate that the ESOM neural network has great efficiency for clustering, especially for the peak flow, and super capability of modelling the flood forecast. The topology maps created from the ESOM are interesting and informative. Copyright © 2007 John Wiley & Sons, Ltd.
Relation:	Hydrological processes 21(6), 741-749
DOI:	10.1002/hyp.6262
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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