Abstract Although various models have been developed for prediction and forecasting of time series in various engineering fields, there is no perfect model to forecast hydrologic time series. In recent decades, Artificial Neural Networks (ANNs) have been very common for prediction and forecasting of hydrologic time series because of their practicality in applications. This study proposed a post-process in an ANN model to improve the forecasting performance by rescaling the errors based on a correlation between observations trained data. The model proposed in this study was examined using precipitation data achieved from different four stations in the United States, and compared with the feedforward networks. It was observed that all error measures used in this study were improved through a rescaling post-process in the model for all stations. The strong point of the model lies in that the correlation approach is very easy to apply.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Monthly precipitation forecasting using rescaling errors


    Beteiligte:
    Kim, Tae-Woong (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2006-03-01


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Image lightness rescaling using sigmoidal contrast enhancement functions

    Braun, G. J. / Fairchild, M. D. | British Library Online Contents | 1999




    Systems and methods for rescaling executable simulation models

    MALONEY PETER J | Europäisches Patentamt | 2022

    Freier Zugriff

    Multi-Wall Recycling / Rescaling Method for Inflow Turbulence Generation

    Boles, John / Choi, Jung-Il / Edwards, Jack et al. | AIAA | 2010