教師資料查詢 | 類別: 期刊論文 | 教師: 張麗秋 LI-CHIU CHANG (瀏覽個人網頁)

標題:Clustering-based hybrid inundation model for forecasting flood inundation depths
學年98
學期2
出版(發表)日期2010/05/01
作品名稱Clustering-based hybrid inundation model for forecasting flood inundation depths
作品名稱(其他語言)
著者Chang, Li-Chiu; Shen, Hung-Yu; Wang, Yi-Fung; Huang, Jing-Yu; Lin, Yen-Tso
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 385(1–4), pp.257–268
摘要Estimation of flood depths and extents may provide disaster information for dealing with contingency and alleviating risk and loss of life and property. We present a two-stage procedure underlying CHIM (clustering-based hybrid inundation model), which is composed of linear regression models and ANNs (artificial neural networks) to build the regional flood inundation forecasting model. The two-stage procedure mainly includes data preprocessing and model building stages. In the data preprocessing stage, K-means clustering is used to categorize the data points of the different flooding characteristics in the study area and to identify the control point(s) from individual flooding cluster(s). In the model building stage, three classes of flood depth forecasting models are built in each cluster: the back-propagation neural network (BPNN) for each control point, the linear regression models for the grids that have highly linear correlation with the control point, and a multi-grid BPNN for the grids that do not have highly linear correlation with the control point. The practicability and effectiveness of the proposed approach is tested in the Dacun Township, Changhua County in Central Taiwan. The results show that the proposed CHIM can continuously and adequately provide 1-h-ahead flood inundation maps that well match the simulation flood inundation results and very effectively reduce 99% CPU time.
關鍵字Flood inundation map;K-means clustering;Back-propagation neural network;Linear regression
語言英文
ISSN0022-1694
期刊性質國外
收錄於SCI;EI;
產學合作
通訊作者Chang, Li-Chiu
審稿制度
國別荷蘭
公開徵稿
出版型式電子版;紙本;
相關連結
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