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Automatic calibration of a rapid flood spreading model using multiobjective optimisations

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Abstract

In order to successfully calibrate a numerical model, multiple criteria should be considered. Multi-objective differential evolution (MODE) and multi-objective particle swarm optimisation (MOPSO) have proved effective in numerous such applications, where most of the techniques relying on the condition of Pareto efficiency to compare different solutions. We describe the performance of two population based search algorithms [nondominated sorting particle swarm optimisation (NSPSO), and nondominated sorting differential evolution (NSDE)] when applied to calibration of a rapid flood spreading model (RFSM). Formulation of an automatic calibration strategy for the RFSM is outline. The simulations show that the both methods possess the ability to find the optimal Pareto front.

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Acknowledgments

The research reported in this paper was conducted as part of the Flood Risk Management Research Consortium. The FRMRC is supported by grant EP/F020511/1 from the Engineering and Physical Sciences Research Council, in partnership with the DEFRA/EA Joint Research Programme on Flood and Coastal Erosion Risk Management, UKWIR, OPW (Ireland) and the Rivers Agency (Northern Ireland). This financial support is gratefully acknowledged.

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Authors and Affiliations

  1. Department of Engineering, Edinburgh University, Edinburgh, EH9 3JL, UK

    Yang Liu

  2. School of the Built Environment, Heriot-Watt University, Edinburgh, EH14 4AS, UK

    Gareth Pender

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  1. Yang Liu
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  2. Gareth Pender
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Correspondence to Yang Liu.

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Liu, Y., Pender, G. Automatic calibration of a rapid flood spreading model using multiobjective optimisations. Soft Comput 17, 713–724 (2013). https://doi.org/10.1007/s00500-012-0944-z

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