Original Paper

Soft Computing

, Volume 17, Issue 4, pp 713-724

First online:

Automatic calibration of a rapid flood spreading model using multiobjective optimisations

  • Yang LiuAffiliated withDepartment of Engineering, Edinburgh University Email author 
  • , Gareth PenderAffiliated withSchool of the Built Environment, Heriot-Watt University

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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.


Parameter estimation Multi-objective optimisation Automatic calibration Rapid flood spreading model