Research on Combination Forecast Mode of Conceptual Hydrological Model

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• 作者：Minglong Dai ; Jianzhong Zhou ; Xiang Liao
• 关键词：Conceptual hydrological model ; Multi ; objective optimization ; Parameter calibration ; Self ; identifying parameters ; Combination forecast
• 刊名：Water Resources Management
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：30
• 期：13
• 页码：4483-4499
• 全文大小：612 KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Earth sciences
  Hydrogeology
  Geotechnical Engineering
  Meteorology and Climatology
  Civil Engineering
  Environment
  
• 出版者：Springer Netherlands
• ISSN：1573-1650
• 卷排序：30

文摘

The calibration and selection of conceptual hydrological model parameters is an important but complex task in runoff forecasting. In order to solve the calibration of conceptual hydrological model parameters, a multi-objective cultural self-adaptive electromagnetism-like mechanism algorithm (MOCSEM) is proposed in this paper. The multi-objective parameter calibration method of runoff forecasting avoids the “averaging effect” and considers both large and small runoffs hydrological features. In this paper, the self-identifying parameter combination forecasting method (SPCFM), a universality combination forecast model, is developed innovatively to improve forecasting precision by using the extreme parameters of Pareto optimal solutions. Finally, MOCSEM is combined with SPCFM to calibrate the parameters of forecasting model and forecast runoff of Leaf River. The results indicate that the proposed methods improve forecast accuracy and provide an effective approach to runoff forecast.