Enforced self-organizing map neural networks for river flood forecasting
學年 95
學期 2
出版(發表)日期 2007-03-01
作品名稱 Enforced self-organizing map neural networks for river flood forecasting
作品名稱(其他語言)
著者 Chang, Fi-john; 張麗秋; Chang, Li-chiu; Wang, Yan-shiang
單位 淡江大學水資源及環境工程學系
出版者 Wiley-Blackwell
著錄名稱、卷期、頁數 Hydrological processes 21(6), 741-749
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.
關鍵字
語言 en
ISSN 0885-6087
期刊性質 國內
收錄於
產學合作
通訊作者
審稿制度
國別 TWN
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/44551 )

機構典藏連結