• DocumentCode
    2318575
  • Title

    Application of genetic algorithm combining operation tree (GAOT) to stream-way transition

  • Author

    Chen, Kuan-ting ; Kou, Chang-huan ; Chen, Li ; Ma, Shih-wei

  • Author_Institution
    Dept. of Civil Eng., Chung Hua Univ., Hsinchu, Taiwan
  • Volume
    5
  • fYear
    2012
  • fDate
    15-17 July 2012
  • Firstpage
    1774
  • Lastpage
    1778
  • Abstract
    The main purpose of this paper is to predict stream-way transition with genetic algorithm (GA) combined with the Operation Tree (OT), called GAOT. Therefore, the downstream stream-way transition according to the upstream conditions is forecasted by GAOT. Five main factors affect the stream-way transition including inflow position, inflow angle, slope, flow discharge, and sand content of suspended sediment were chosen as input variables. We selected two important cross sections nearby a damaged bridge of Ta-Chia River in Taiwan as a case study. The results show that GAOT has better performance than the traditional linear regression (LR) method.
  • Keywords
    genetic algorithms; regression analysis; rivers; trees (mathematics); GAOT; Ta-Chia River; Taiwan; damaged bridge; downstream stream-way transition; flow discharge; genetic algorithm combining operation tree; inflow angle; inflow position; linear regression method; sand content; slope; suspended sediment; Abstracts; Biological information theory; Biological system modeling; Geology; Testing; Training; Genetic algorithm; Linear regression; Operation tree; Stream-way transition; Ta-Chia River;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
  • Conference_Location
    Xian
  • ISSN
    2160-133X
  • Print_ISBN
    978-1-4673-1484-8
  • Type

    conf

  • DOI
    10.1109/ICMLC.2012.6359644
  • Filename
    6359644