• DocumentCode
    467726
  • Title

    Application of Genetic Programming to Stream-Flow Extension

  • Author

    Wang, Tai-Sheng ; Chen, Li ; Wu, Ming-Ming

  • Author_Institution
    Chung Hua Univ., Hsinchu
  • Volume
    2
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    974
  • Lastpage
    977
  • Abstract
    The main purpose of this paper is to present the genetic programming (GP) and apply it to extend the flow records (y) according to the nearby stream-flow station (x). Based on GP, the relationships between x and y can be expressed as parse trees. A fittest function type will be obtained automatically from this method. The model is applied to extend the annual stream flow records according to the nearby stream flow station. The results show that GP has better performance than the traditional linear regression method.
  • Keywords
    ecology; genetic algorithms; genetic programming; regression method; stream-flow extension; Artificial neural networks; Biological cells; Civil engineering; Cybernetics; Genetic algorithms; Genetic programming; Linear regression; Machine learning; Performance gain; Signal processing algorithms; Genetic algorithms; Genetic programming; Hydrological data analysis; Regression method; System identification;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370283
  • Filename
    4370283
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=467726