Soft Computing

, Volume 17, Issue 4, pp 713–724 | Cite as

Automatic calibration of a rapid flood spreading model using multiobjective optimisations

Original Paper

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.

Keywords

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

References

  1. Chen AS, Evans B, Djordjević S, Savić DA (2012) Multi-layered coarse grid modelling in 2D urban flood simulations. J Hydrol 426–427:1–16Google Scholar
  2. Coello Coello CA, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279CrossRefGoogle Scholar
  3. Cooper VA, Nguyen VTV, Nicell JA (1997) Evaluation of global optimisation methods for conceptual rainfall-runoff model calibration. Water Sci Technol 36(5):53–60CrossRefGoogle Scholar
  4. Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  5. Defra and Agency (2006) The flood risks to people methodology, flood risks to people phase 2. FD2321 Technical Report 1, Wallingford HR et al. did the report for Defra/EA Flood and Coastal Defence R&D ProgrammeGoogle Scholar
  6. Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation. Washington, DC, pp 84–88Google Scholar
  7. Guo YF, Keedwell E, Walters G, Khu ST (2007) Hybridizing cellular automata principles and NSGAII for multi-objective design of urban water networks. In: Proceedings of evolutionary multi-criterion optimization, vol 4403, pp 546–559Google Scholar
  8. Janssen PHM, Heuberger PSC (1995) Calibration of process-oriented models. Ecol Model 83:55–66CrossRefGoogle Scholar
  9. Kennedy J, Eberhart R (1995) Particle swarm optimisation. In: Proceedings of the IEEE international conference on neural networks, pp 1942–1945Google Scholar
  10. Knowles J, Corne D (2002) On metrics for comparing non-dominated sets. In: Congress on evolutionary computation, pp 711–716Google Scholar
  11. Krupka M, Wallis S, Pender G, Neélz S (2007) Some practical aspects of flood inundation modelling. Transport phenomena in hydraulics, Publications of the Institute of Geophysics, Polish Academy of Sciences. E-7 (401), 129–135Google Scholar
  12. Lhomme J, Sayers P, Gouldby B, Samuels P, Wills M, Mulet-Marti J (2008) Recent development and application of a rapid flood spreading method. River Flow 2008. Oxford, pp 15–24Google Scholar
  13. Liu Y (2009) Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm. Expert Syst Appl 36(5):9533–9538CrossRefGoogle Scholar
  14. Liu Y, Pender G, Neélz S (2009) Improving the performance of fast inundation models using v-support vector regression and particle swarm optimisation. In: 33rd IAHR 2009 Congress, pp 1436–1443Google Scholar
  15. Lorio A, Li X (2004) Solving rotated multi-objective optimization problems using differential evolution. In Webb GI, Yu X (eds) In: Proceedings of the 17th joint Australian conference on artificial intelligence, pp 861–872Google Scholar
  16. Madsen H (2000) Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. J Hydrol 235:276–288Google Scholar
  17. Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33:161–174CrossRefGoogle Scholar
  18. Storn R, Price K (1995) Differential evolution: a simple and efficient adaptive scheme for global optimisation over continuous spaces. Technical Report TR-95-012. International Computer Science Institute, BerkleyGoogle Scholar
  19. Sun HJ, Peng CH, Guo JF, Li HS (2009) Non-dominated sorting differential evolution algorithm for multi-objective optimal integrated generation bidding and scheduling. In: IEEE international conference on intelligent computing and intelligent systems, vol 1, pp 372–376Google Scholar
  20. Yapo PO, Gupta HV, Sorooshian S (1998) Multi-objective global optimisation for hydrologic models. J Hydrol 204:83–97CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of EngineeringEdinburgh UniversityEdinburghUK
  2. 2.School of the Built EnvironmentHeriot-Watt UniversityEdinburghUK

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