Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model

Article

Abstract

Conceptual rainfall-runoff modelling is a widely-used approach for rainfall-runoff simulation in streamflow forecasting. The objective of this paper is to introduce an improved non-dominated sorting genetic algorithm-II (NSGA-II) for multi-objective automatic calibration of a hydrologic model. The orthogonal design based initialization technique is exploited to produce a more uniformly-distributed initial population. At the same time, a chaotic crossover operator as well as a chaotic mutation operator are presented to avoid trapping into local minima and to obtain high quality solutions. Finally, a multi-criteria decision-making (MCDM) approach combing Shannon entropy weighting method and an improved technique for order preference by similarity to ideal solution (ITOPSIS) based on projection is introduced to prioritize the Pareto optimal solutions and select the comprehensive optimal solution as a follow-up step. Hydrological data from two river basins named the Leaf and Muma River basins are exploited to test the ability of the orthogonal chaotic NSGA-II (OCNSGA-II) for solving the multi-objective HYMOD (MO-HYMOD) problem. The results demonstrate that the OCNSGA-II can obtain better-distributed Pareto optimal front and thus can be exploited as an effective alternative approach for the multi-objective automatic calibration of hydrologic model.

Keywords

Conceptual rainfall-runoff model Multi-objective optimization NSGA-II Multi-criteria decision-making Projection method 

Notes

Acknowledgements

This work is supported by the Key Program of the Major Research Plan of the National Natural Science Foundation of China (No. 91547208), the National Natural Science Foundation of China (No.51579107), the National Key R&D Program of China (2016YFC0402708, 2016YFC0401005) and special thanks are given to the anonymous reviewers and editors for their constructive comments.

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Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Tian Peng
    • 1
    • 2
  • Jianzhong Zhou
    • 1
    • 2
  • Chu Zhang
    • 1
    • 2
  • Na Sun
    • 1
    • 2
  1. 1.School of Hydropower and Information EngineeringHuazhong University of Science and TechnologyWuhanChina
  2. 2.Hubei Key Laboratory of Digital Valley Science and TechnologyWuhanChina

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