• DocumentCode
    527795
  • Title

    Modeling stand density index based on artificial neural network

  • Author

    Huang, Jiarong ; Gao, Guangqin ; Meng, Xianyu ; Guan, Yuxiu

  • Author_Institution
    Coll. of Forestry, Henan Agric. Univ., Zhengzhou, China
  • Volume
    4
  • fYear
    2010
  • fDate
    10-12 Aug. 2010
  • Firstpage
    1734
  • Lastpage
    1736
  • Abstract
    A stand density index BP model was created with artificial neural network modeling technology, by taking the average of diameter at breast height as the input variable, and stand number density as the output variable, in Masson pine planted forest. Through training and optimum seeking, the idea model structure is 1:2:1, the fitting accuracy is 98.67%. As a comparison, a Reineke stand density index model was created and fitted with regression analysis method and same sample data, the fitting accuracy is 97.76%. The results comparing with BP model and Reineke model indicate that the artificial neural network is a more effective stand density index modeling technique.
  • Keywords
    backpropagation; forestry; neural nets; regression analysis; BP model; Masson pine planted forest; Reineke stand density index model; artificial neural network modeling technology; regression analysis; Artificial neural networks; Biological system modeling; Data models; Fitting; Indexes; Mathematical model; Neurons; Pinus massoniana; artificial neural network; model; stand density index;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2010 Sixth International Conference on
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-5958-2
  • Type

    conf

  • DOI
    10.1109/ICNC.2010.5584350
  • Filename
    5584350