期刊論文
| 學年 | 101 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2013-03-01 |
| 作品名稱 | Online multistep-ahead inundation depth forecasts by recurrent NARX networks |
| 作品名稱（其他語言） | |
| 著者 | Shen, Hung-Yu; Chang, Li-Chiu |
| 單位 | 淡江大學水資源及環境工程學系 |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Hydrology and Earth Systems Science 17(3), pp.935-945 |
| 摘要 | Various types of artificial neural networks (ANNs) have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events. |
| 關鍵字 | |
| 語言 | en_US |
| ISSN | 1027-5606 1607-7938 |
| 期刊性質 | 國外 |
| 收錄於 | SCI EI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 是 |
| 國別 | DEU |
| 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/987654321/93125 ) |