This paper outlines the application of multiple linear regression and three different data-driven modeling techniques to river level forecasting for the River Ouse Catchment in northern England. Lead times of 6 and 24 hours ahead were modelled. The results show that the data driven approaches generally outperformed the statistical approach and that M5 model trees have great potential for the development of transparent river level forecasting models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Rainfall-Runoff Modelling using Data Driven and Statistical Methods


    Contributors:
    Khan, Saadat (author) / See, Linda (author)


    Publication date :

    2006-09-01


    Size :

    267948 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Data-driven approaches for estimating uncertainty in rainfall-runoff modelling

    Shrestha, D.L. / Solomatine, D.P. | British Library Online Contents | 2008


    Rainfall-runoff modelling: The primer

    Bates, P. | British Library Online Contents | 2003


    Modelling runoff for extreme rainfall events on large catchments using TELEMAC-2D

    Travert, Jean-Paul / Taccone, Florent / Bacchi, Vito | HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2022

    Free access


    Hydrologic vs Hydraulic Rainfall-Runoff Modeling

    Horst, Michael | British Library Conference Proceedings | 2022