• Login
    View Item 
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Construction + Engineering
    • Construction + Engineering Journal Articles
    • View Item
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Construction + Engineering
    • Construction + Engineering Journal Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Use of gene expression programing for multi-model combination of rainfall-runoff models

    Fernando, Achela; Shamseldin, Asaad; Abrahart, Robert

    Thumbnail
    Share
    View fulltext online
    Fernando - Gene expression.pdf (775.4Kb)
    Date
    2012
    Citation:
    Fernando, A., Shamseldin, A., & Abrahart, R. (2011). Use of gene expression programing for multi-model combination of rainfall- runoff models. Journal of Hydrologic Engineering 17(9), 975–985. doi: http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000533
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/1887
    Abstract
    This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments. The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are ‘black-box’ models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method. The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.
    Keywords:
    rainfall-runoff model, symbolic regression, model combination, gene expression programming
    ANZSRC Field of Research:
    090509 Water Resources Engineering
    Copyright Holder:
    American Society of Civil Engineers

    Copyright Notice:
    All rights reserved
    Available Online at:
    http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000533
    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. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
    Metadata
    Show detailed record
    This item appears in
    • Construction + Engineering Journal Articles [63]

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga

    Usage

    Downloads, last 12 months
    66
     
     

    Usage Statistics

    For this itemFor the Research Bank

    Share

    About

    About Research BankContact us

    Help for authors  

    How to add research

    Register for updates  

    LoginRegister

    Browse Research Bank  

    EverywhereInstitutionsStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaboratorThis CollectionStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaborator

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga