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Stand basal area modelling for Chinese fir plantations using an artificial neural network model

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Abstract

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function, non-Gaussian distributions, multicollinearity, outliers and noise in the data. The problems of back-propagation models using artificial neural networks include determination of the structure of the network and over-learning courses. According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province, a back-propagation artificial neural network model (BPANN) and a support vector machine model (SVM) for basal area of Chinese fir (Cunninghamia lanceolata) plantations were constructed using four kinds of prediction factors, including stand age, site index, surviving stem numbers and quadratic mean diameters. Artificial intelligence methods, especially SVM, could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models. SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

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Author information

Author notes

  1. Shaohui Che and Xiaohong Tan are co-first author.

Authors and Affiliations

  1. Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, People’s Republic of China

    Shaohui Che, Xiaohong Tan, Congwei Xiang, Xiaoyan Hu, Xiongqing Zhang, Aiguo Duan & Jianguo Zhang

  2. Beijing Institute of Economics and Management, Beijing, 100091, People’s Republic of China

    Shaohui Che

  3. Subtropical Forestry Experimental Central, Chinese Academy of Forestry, Fenyi, 336600, People’s Republic of China

    Jianjun Sun

Authors
  1. Shaohui Che
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  2. Xiaohong Tan
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  3. Congwei Xiang
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  4. Jianjun Sun
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  5. Xiaoyan Hu
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  6. Xiongqing Zhang
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  7. Aiguo Duan
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  8. Jianguo Zhang
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Corresponding author

Correspondence to Aiguo Duan.

Additional information

Project funding: The work was supported by the National Scientific and Technological Task in China (Nos. 2015BAD09B0101, 2016YFD0600302), National Natural Science Foundation of China (No. 31570619), and the Special Science and Technology Innovation in Jiangxi Province (No. 201702).

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Corresponding editor: Tao Xu.

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Che, S., Tan, X., Xiang, C. et al. Stand basal area modelling for Chinese fir plantations using an artificial neural network model. J. For. Res. 30, 1641–1649 (2019). https://doi.org/10.1007/s11676-018-0711-9

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  • DOI: https://doi.org/10.1007/s11676-018-0711-9

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