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Evolutionary Multiobjective Optimization for Selecting Members of an Ensemble Streamflow Forecasting Model


Darwin Brochero, Christian Gagné and François Anctil


Abstract - We are proposing to use the Nondominated Sorting Genetic Algorithm II (NSGA-II) for optimizing a hydrological forecasting model of 800 simultaneous streamflow predictors. The optimization is based on the selection of the best 48 predictors from the 800 that jointly define the ``best'' ensemble in terms of two probabilistic criteria. Results showed that the difficulties in simplifying the ensembles mainly originate from the preservation of the system reliability. We conclude that Pareto fronts generated with NSGA-II allow the development of a decision process based explicitly on the trade-off between different probabilistic properties. In other words, evolutionary multiobjective optimization offers more flexibility to the operational hydrologists than a priori methods that produce only one selection.

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Bibtex:

@inproceedings{Brochero977,
    author    = { Darwin Brochero and Christian Gagné and François Anctil },
    title     = { Evolutionary Multiobjective Optimization for Selecting Members of an Ensemble Streamflow Forecasting Model },
    booktitle = { Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2013) },
    year      = { 2013 },
    month     = { July 6-10 },
    location  = { Amsterdam, The Netherlands }
}

Dernière modification: May 22 2013 1:49PM par cgagne

     
   
   

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