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Analysis of prediction algorithm for forest land spatial evolution trend in rural planning

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Abstract

This study is to find the factors that affect the spatial change of forest land and purposefully predict the evolution trend of forest land space, so as to facilitate the rural planning work. The rural forest land situation in Zhangjiakou City of Hebei Province is analyzed, and the future evolution and development of forest space are predicted through analysis of correlation between the forest land influencing factors and the forest land productivity. Meanwhile, the multiple linear regression (MLR) prediction algorithm and support vector machine (SVM) are compared to obtain a more accurate prediction algorithm, which provides a strong basis for rural planning. The research results show that the annual rainfall and rainfall erosion have poor correlation with the spatial evolution of forest land relatively; while the average annual temperature is negatively correlated with annual rainfall and the rainfall erosivity. In addition, the soil erosion and terrain undulation of forest land have higher correlations with the rainfall erosivity due to abundant rainfall. The steeper the slope, the less human interference. What’s more, the prediction value of SVM is closer to the actual value with smaller absolute error, so it is more accurate than MLR. Therefore, research on the prediction algorithm provides new ideas for enriching the prediction algorithms of the spatial evolution trend, and is of great significance for improving the forest resource reserve capacity and meeting more forest resource demand in China. In addition, it can optimize the natural environmental quality, so it can be applied to rural planning and construction.

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Acknowledgement

Supported by the Stragegic Priority Research Program of the Chinese Academy of Sciences, Grant No. XDA19040501.

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Authors and Affiliations

  1. School of Architecture and Art, Central South University, Changsha, 410082, China

    Xiujuan Jiang, Nan Zhang & Hui Liu

  2. School of Civil Engineering and Architecture, Zhengzhou University of Aeronautics, Zhengzhou, 450051, China

    Xiujuan Jiang

  3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101, China

    Jinchuan Huang

  4. School of Architecture and Art Design, Hunan University of Science and Technology, Xiangtan, 411100, China

    Ping Zhang

  5. College of Civil Engineering and Architecture, Hunan Institute of Science and Technology, Yueyang, 414000, China

    Hui Liu

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  1. Xiujuan Jiang
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  2. Nan Zhang
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  3. Jinchuan Huang
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  4. Ping Zhang
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  5. Hui Liu
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Correspondence to Xiujuan Jiang.

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Jiang, X., Zhang, N., Huang, J. et al. Analysis of prediction algorithm for forest land spatial evolution trend in rural planning. Cluster Comput 24, 195–203 (2021). https://doi.org/10.1007/s10586-020-03227-7

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  • DOI: https://doi.org/10.1007/s10586-020-03227-7

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