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Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model

Author

Listed:
  • Yi Liu
  • Jun Guo
  • Huaiwei Sun
  • Wei Zhang
  • Yueran Wang
  • Jianzhong Zhou

Abstract

Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS) is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP) is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II) on a daily streamflow forecasting model based on support vector machine (SVM). The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

Suggested Citation

  • Yi Liu & Jun Guo & Huaiwei Sun & Wei Zhang & Yueran Wang & Jianzhong Zhou, 2016. "Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-13, August.
    • Handle: RePEc:hin:jnlmpe:8215308
    DOI: 10.1155/2016/8215308
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    Cited by:

    1. Lili Wang & Yanlong Guo & Manhong Fan, 2022. "Improving Annual Streamflow Prediction by Extracting Information from High-frequency Components of Streamflow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4535-4555, September.

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