期刊論文
| 學年 | 110 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2022-06-18 |
| 作品名稱 | Spatial-temporal flood inundation nowcasts by fusing machine learning methods and principal component analysis |
| 作品名稱（其他語言） | |
| 著者 | Chang, Li-chiu |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Journal of Hydrology 612, 128086 |
| 摘要 | The frequency and severity of floods have noticeably increased worldwide in the last decades due to climate change and urbanization. This study aims to build an urban flood warning system for reducing the impact of flood disasters. A great number of storm-induced rainfall data were collected in Taipei, Taiwan, and the corresponding 2-D inundation maps were simulated for illustrating urban rainfall-flood inundation processes. We proposed a novel urban flood forecast methodology framed by machine learning and statistical techniques to mine the spatial–temporal features between rainfall patterns and inundation maps for making multi-step-ahead regional flood inundation forecasts. The proposed methodology (PCA-SOM-NARX) integrated the advantages of Principal Component Analysis (PCA), Self-Organizing Map (SOM), and Nonlinear Autoregressive with Exogenous Inputs (NARX). PCA was used to extract principal components representing the different spatial distributions of urban inundation. SOM was used to cluster high dimensional inundation datasets to form a two-dimensional topological feature map. NARX was used to establish multi-step-ahead flood forecast models for the next hour at a 10-minute scale. The results show that the PCA-SOM-NARX approach not only produced more stable and accurate multi-step-ahead forecasts on flood inundation depth but was also more indicative of the spatial distribution of inundation caused by torrential rain events, compared to the SOM-NARX approach (the benchmark). The results demonstrate the proposed methodology can adequately grasp the inundation status associated with different rainfall distributions to reliably and accurately forecast regional flood inundation depths, which can help decision makers respond to flooding earlier and mitigate flood disasters. |
| 關鍵字 | Principal Component Analysis (PCA);Self-Organizing Map (SOM);Nonlinear Autoregressive with Exogenous Inputs (NARX);Spatio-temporal analysis of inundation;Urban flood forecasting |
| 語言 | en_US |
| ISSN | 0022-1694 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 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/987654321/124222 ) |