期刊論文
| 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 | 否 |
| 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 ) |