Water Resources Management

, Volume 30, Issue 13, pp 4483–4499 | Cite as

Research on Combination Forecast Mode of Conceptual Hydrological Model

  • Minglong DaiEmail author
  • Jianzhong ZhouEmail author
  • Xiang Liao


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.


Conceptual hydrological model Multi-objective optimization Parameter calibration Self-identifying parameters Combination forecast 



This work is supported by the National Natural Science Foundation of China (No. 51239004).


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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Bureau of HydrologyChangjiang Water Resources CommissionWuhanPeople’s Republic of China

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