A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, Taiwan

Authors

  • Pei-Hao Li,

    Corresponding author
    1. Institute of Environmental Engineering, National Chiao Tung University, Hsinchu, 30090, Taiwan, ROC
    2. Department of Earth and Environmental Engineering, Columbia University, NY 10027, USA
    • Institute of Environmental Engineering, National Chiao Tung University, Hsinchu, 30090, Taiwan, ROC.
    Search for more papers by this author
  • Hyun-Han Kwon,

    1. Department of Earth and Environmental Engineering, Columbia University, NY 10027, USA
    Search for more papers by this author
  • Liqiang Sun,

    1. International Research Institute for Climate and Society, Columbia University, NY 10964, USA
    Search for more papers by this author
  • Upmanu Lall,

    1. Department of Earth and Environmental Engineering, Columbia University, NY 10027, USA
    2. International Research Institute for Climate and Society, Columbia University, NY 10964, USA
    Search for more papers by this author
  • Jehng-Jung Kao

    1. Institute of Environmental Engineering, National Chiao Tung University, Hsinchu, 30090, Taiwan, ROC
    Search for more papers by this author

Abstract

The uncertainty of the availability of water resources during the boreal winter has led to significant economic losses in recent years in Taiwan. A modified support vector machine (SVM) based prediction framework is thus proposed to improve the predictability of the inflow to Shihmen reservoir in December and January, using climate data from the prior period. Highly correlated climate precursors are first identified and adopted to predict water availability in North Taiwan. A genetic algorithm based parameter determination procedure is implemented to the SVM parameters to learn the non-linear pattern underlying climate systems more flexibly. Bagging is then applied to construct various SVM models to reduce the variance in the prediction by the median of forecasts from the constructed models. The enhanced prediction ability of the proposed modified SVM-based model with respect to a bagged multiple linear regression (MLR), simple SVM, and simple MLR model is also demonstrated. The results show that the proposed modified SVM-based model outperforms the prediction ability of the other models in all of the adopted evaluation scores. Copyright © 2009 Royal Meteorological Society

Ancillary