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Parameter Identification of RVM Runoff Forecasting Model Based on Improved Particle Swarm Optimization

  • Yuzhi Shi
  • Haijiao Liu
  • Mingyuan Fan
  • Jiwen Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7928)

Abstract

Runoff forecasting which subjects to model pattern and parameter optimization, has an important significance of reservoir scheduling and water resources management decision-makings. This paper proposed a new forecasting model coupled phase space reconstruction technology with relevance vector machine, and its model parameters is optimized by an improved PSO algorithm. The monthly runoff time series from 1953 to 2003 at Manwan station is selected as an example. The results show that the improved PSO has efficient optimization performance and the proposed forecasting model could obtain higher prediction accuracy.

Keywords

Improved PSO algorithm Relevance vector machine Phase space reconstruction parameter identification Runoff forecasting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yuzhi Shi
    • 1
  • Haijiao Liu
    • 1
  • Mingyuan Fan
    • 1
  • Jiwen Huang
    • 1
  1. 1.Water Resources Research Institute of Shandong ProvinceJinanChina

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