Enforced self-organizing map neural networks for river flood forecasting

淡江大學機構典藏 > 工學院 > 水資源及環境工程學系暨研究所 > 期刊論文 > Item 987654321/44551

請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/44551

題名:	Enforced self-organizing map neural networks for river flood forecasting
作者:	Chang, Fi-john;張麗秋;Chang, Li-chiu;Wang, Yan-shiang
貢獻者:	淡江大學水資源及環境工程學系
日期:	2007-03-01
上傳時間:	2010-03-26 16:17:20 (UTC+8)
出版者:	Wiley-Blackwell
摘要:	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.
關聯:	Hydrological processes 21(6), 741-749
DOI:	10.1002/hyp.6262
顯示於類別:	[水資源及環境工程學系暨研究所] 期刊論文

文件中的檔案:

檔案	描述	大小	格式	瀏覽次數
		0Kb	Unknown	404	檢視/開啟
Chang_et_al-2007-Hydrological_Processes.pdf		335Kb	Adobe PDF	1	檢視/開啟

在機構典藏中所有的資料項目都受到原著作權保護.

TAIR相關文章