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Differential Hydrological Grey Model (DHGM) with self-memory function and its application to flood forecasting

Science China(Technological Sciences) 2009年第4期52卷 1039-1049页

作者： CHEN XiangDong1, XIA Jun1,2 & XU Qian3 1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 2 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China 3 School of Hydrology and Water Resources, Hohai University, Nanjing 210098, China Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan China School of Hydrology and Water Resources Hohai University Nanjing China

This paper addresses a problem of flood forecasting with the self-memory function. Considering flood forecasting’s uncertainty and updating demand, a hybrid hydrological model, namely Differential Hy- drological Grey Model with self-memory function (DHGM-SM), is developed. The model has two fold features. One is to establish a self-memorization equation linked with DHGM, that could extract useful information from past data series and realize updating of hydrological dynamic process. The other is that this model has higher efficiency relative to original hydrological model without self-memory func- tion. This approach was applied to river flow forecasting of two representative basins in Tunxi of South China and Daqinggou of North China. It is shown that this hybrid method has satisfactory forecasting accuracy by examination of both calibration and validation.

关键词： differential grey model the self-memorization principle real-time forecasting faded-memory recursive least square method

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