Research on Combination Forecast Mode of Conceptual Hydrological Model

Minglong Dai (), Jianzhong Zhou () and Xiang Liao
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Minglong Dai: Huazhong University of Science and Technology
Jianzhong Zhou: Huazhong University of Science and Technology
Xiang Liao: Huazhong University of Science and Technology

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 13, No 2, 4483-4499

Abstract: Abstract 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.

Keywords: Conceptual hydrological model; Multi-objective optimization; Parameter calibration; Self-identifying parameters; Combination forecast (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-016-1401-z

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