期刊論文
| 學年 | 102 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2013-08-01 |
| 作品名稱 | Reinforced Recurrent Neural Networks for Multi-Step-Ahead Flood Forecasts |
| 作品名稱（其他語言） | |
| 著者 | Chen, Pin-An; Chang, Li-Chiu; Chang, Fi-John |
| 單位 | 淡江大學水資源及環境工程學系 |
| 出版者 | Amsterdam: Elsevier BV |
| 著錄名稱、卷期、頁數 | Journal of Hydrology 497, pp.71-79 |
| 摘要 | Considering true values cannot be available at every time step in an online learning algorithm for multi-step-ahead (MSA) forecasts, a MSA reinforced real-time recurrent learning algorithm for recurrent neural networks (R-RTRL NN) is proposed. The main merit of the proposed method is to repeatedly adjust model parameters with the current information including the latest observed values and model’s outputs to enhance the reliability and the forecast accuracy of the proposed method. The sequential formulation of the R-RTRL NN is derived. To demonstrate its reliability and effectiveness, the proposed R-RTRL NN is implemented to make 2-, 4- and 6-step-ahead forecasts in a famous benchmark chaotic time series and a reservoir flood inflow series in North Taiwan. For comparison purpose, three comparative neural networks (two dynamic and one static neural networks) were performed. Numerical and experimental results indicate that the R-RTRL NN not only achieves superior performance to comparative networks but significantly improves the precision of MSA forecasts for both chaotic time series and reservoir inflow case during typhoon events with effective mitigation in the time-lag problem. |
| 關鍵字 | Reinforced real-time recurrent learning (R-RTRL) algorithm; Recurrent neural network (RNN); Multi-step-ahead forecast; Flood forecast |
| 語言 | en |
| ISSN | 0022-1694 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | Chang, Fi-John |
| 審稿制度 | 是 |
| 國別 | NLD |
| 公開徵稿 | |
| 出版型式 | ,電子版,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/93124 ) |