Automatic calibration of a rapid flood spreading model using multiobjective optimisations

@article{Liu2013AutomaticCO,
  title={Automatic calibration of a rapid flood spreading model using multiobjective optimisations},
  author={Yang P. Liu and Gareth Pender},
  journal={Soft Computing},
  year={2013},
  volume={17},
  pages={713-724}
}
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… Expand
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8 Citations
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Methods Citations
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8 Citations

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References

SHOWING 1-10 OF 18 REFERENCES
Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm
  • Y. Liu
  • Computer Science
  • Expert Syst. Appl.
  • 2009
TLDR
A new non-dominated sorting particle swarm optimisation (NSPSO), is proposed, that combines the operations of a known MOGA NSGA-II and the other advanced operations (selection and mutation operations) with a single particle swarm Optimisation (PSO). Expand
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives.
TLDR
A balanced aggregated objective function is proposed, which provides a compromise solution that puts equal weights to the different objectives in the multi-objective calibration problem. Expand
Non-dominated sorting differential evolution algorithm for multi-objective optimal integrated generation bidding and scheduling
TLDR
A new multi-objective differential evolution optimization algorithm, which integrated Pareto non-dominant sorting and differential evolution algorithm and improved individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, was designed to achieve Pare to optimal set of this model. Expand
Hybridizing Cellular Automata Principles and NSGAII for Multi-objective Design of Urban Water Networks
TLDR
CAMOGA can outperform the standard multi-objective genetic algorithm in terms of optimization efficiency and quality of the obtained Pareto fronts, and is applied to four large real-world networks. Expand
Handling multiple objectives with particle swarm optimization
TLDR
An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems. Expand
Multi-objective global optimization for hydrologic models
TLDR
The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objectives global optimization problem, is presented in this paper and is an extension of the successful SCE-UA single-objective global optimization algorithm. Expand
Evaluation of global optimization methods for conceptual rainfall-runoff model calibration
The calibration of conceptual rainfall runoff (CRR) models is an optimization problem whose objective is to determine the values of the model parameters which provide the best fit between observed… Expand
A fast and elitist multiobjective genetic algorithm: NSGA-II
TLDR
This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently. Expand
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
TLDR
Simulation results on five difficult test problems show that the proposed NSGA-II, in most problems, is able to find much better spread of solutions and better convergence near the true Pareto-optimal front compared to PAES and SPEA--two other elitist multi-objective EAs which pay special attention towards creating a diverse Paretimal front. Expand
Recent development and application of a rapid flood spreading method
Flood risk analysis increasingly involves the integration of a full range of loading conditions as well as multiple defence system states, overlaid by uncertainty analysis. This type of analysis… Expand
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