Abstract This paper presents a Least-Square Support Vector Machine (LS-SVM) approach for forecasting nonlinear hydrological time series. LS-SVM is a machine-learning algorithm firmly based on the statistical learning theory. The objective of this paper is to examine the feasibility of using LS-SVM in the forecasting of nonlinear hydrological time series by comparing it with a statistical method such as Multiple Linear Regression (MLR) and a heuristic method such as a Neural Network using Back-Propagation (NNBP). And the performance of prediction model is also dependent on the degrees of linearity (or persistency) of data, not only on the performance of model itself. Thus, we would clearly verify that prediction performance of three models according to linear extent using daily water demand and daily inflow of dam data. In the experimental results, LS-SVM showed superior forecasting accuracies and performances to those of MLR and NNBP and LS-SVM demonstrated better forecasting efficiency in nonlinear hydrological time series using Relative Correlation Coefficient (RCC) which is a relative measure of forecasting efficiency with different persistency.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Forecasting performance of LS-SVM for nonlinear hydrological time series


    Beteiligte:
    Hwang, Seok Hwan (Autor:in) / Ham, Dae Heon (Autor:in) / Kim, Joong Hoon (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2012-06-29


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Forecasting performance of LS-SVM for nonlinear hydrological time series

    Hwang, Seok Hwan / Ham, Dae Heon / Kim, Joong Hoon | Online Contents | 2012


    The Simulation and Subseasonal Forecasting of Hydrological Variables

    Randal Koster / Yuna Lim / Yujin Zeng et al. | NTRS


    Review of Time Series Traffic Forecasting Methods

    Wang, Linkai / Chen, Jing / Wang, Wei et al. | IEEE | 2022



    Trends in Hydrological Series: Methods and Application

    Detzel, Daniel / Mine, Miriam / Kuratorium für Forschung im Küsteningenieurwesen (KFKI) | HENRY – Bundesanstalt für Wasserbau (BAW) | 2014

    Freier Zugriff