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    Multi-model forecasting: Using gene expression programming to develop explicit equations for rainfall-runoff modelling combinations

    Fernando, Achela; Abrahart, Robert; Shamseldin, Asaad

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    Date
    2009
    Citation:
    Abrahart, R.J., Shamseldin, A.Y., & Fernando, D.A.K. (2009). Multi-model forecasting: Using gene expression programming to develop explicit equations for rainfall-runoff modelling combinations [Abstract]. Geophysical Research Abstracts, 11, EGU2009-13886. Available from http://meetingorganizer.copernicus.org/EGU2009/EGU2009-13886.pdf
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/1888
    Abstract
    Two previous studies have evaluated eight multi-model forecasting strategies that combined hydrological forecasts for contrasting catchments: the River Ouse in Northern England and the Upper River Wye in Central Wales. The level and discharge inputs that were combined comprised a mixed set of independent forecasts produced using different modelling methodologies. Earlier multi-model combination approaches comprised: arithmetic-averaging, a probabilistic method in which the best model from the last time step is used to generate the current forecast, two different neural network operations, two different soft computing methodologies, a regression tree solution and instance-based learning. The nature and properties of past combination functions was not however explored and no theoretical outcome to support subsequent improvements resulted. This paper presents a pair of counterpart mathematical equations that were evolved in GeneXproTools 4.0: a powerful software package that is used to perform symbolic regression operations using gene expression programming. The results suggest that simple mathematical equations can be used to perform efficacious multi-model combinations; that similar mathematical solutions can be developed to fulfil different hydrological modelling requirements; and that the procedure involved produces mathematical outcomes that can be explained in terms of minimalist problem-solving strategies.
    Keywords:
    rainfall-runoff model, gene expression programming, hydrological modelling
    ANZSRC Field of Research:
    090702 Environmental Engineering Modelling
    Copyright Holder:
    Authors
    Available Online at:
    http://meetingorganizer.copernicus.org/EGU2009/EGU2009-13886.pdf
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use: Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Construction + Engineering Conference Papers [198]

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