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 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… 
A Novel Fast Optimisation Algorithm Using Differential Evolution Algorithm Optimisation and Meta-Modelling Approach
TLDR
The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls with a very small number of actual evaluations, compared to these traditional optimisation algorithms.
A flood inundation modelling using v-support vector machine regression model
DIGITAL TERRAIN MODELS IN SIBERIAN CITIES AND UTILITY NETWORKS DESIGN
  • Y. Voronov
  • Computer Science
    Interexpo GEO-Siberia
  • 2021
TLDR
One of the important directions of innovative technologies in the urban economy, application of digital terrain models in the design, development and operation of utility networks is considered, in which each facies (local catchment) is replaced by an equivalent inclined funnel of surface runoff.
Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors
This paper focusses on a discrete-time neural identifier applied to a linear induction motor (LIM) model, whose model is assumed to be unknown. This neural identifier is robust in presence of
Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method
TLDR
A type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption is presented and can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.
Work Package: WP2 Urban Flood Modelling Document Name: Future impacts of urban growth and climate change on flood probability
TLDR
The user thereof uses the information at its sole risk and neither the European Commission nor any member of the CORFU Consortium is liable for any use that may be made of the information.

References

SHOWING 1-10 OF 18 REFERENCES
Non-dominated sorting differential evolution algorithm for multi-objective optimal integrated generation bidding and scheduling
  • H. Sun, C. Peng, J. Guo, H. S. Li
  • Computer Science
    2009 IEEE International Conference on Intelligent Computing and Intelligent Systems
  • 2009
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.
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.
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.
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.
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.
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
...
...