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融合半变异函数的空间随机森林插值方法

王铭鑫 范超 高秉博 任周鹏 李发东

王铭鑫, 范超, 高秉博, 任周鹏, 李发东. 融合半变异函数的空间随机森林插值方法[J]. 中国生态农业学报 (中英文), 2022, 30(3): 451−457 doi: 10.12357/cjea.20210628
引用本文: 王铭鑫, 范超, 高秉博, 任周鹏, 李发东. 融合半变异函数的空间随机森林插值方法[J]. 中国生态农业学报 (中英文), 2022, 30(3): 451−457 doi: 10.12357/cjea.20210628
WANG M X, FAN C, GAO B B, REN Z P, LI F D. A spatial random forest interpolation method with semi-variogram[J]. Chinese Journal of Eco-Agriculture, 2022, 30(3): 451−457 doi: 10.12357/cjea.20210628
Citation: WANG M X, FAN C, GAO B B, REN Z P, LI F D. A spatial random forest interpolation method with semi-variogram[J]. Chinese Journal of Eco-Agriculture, 2022, 30(3): 451−457 doi: 10.12357/cjea.20210628

融合半变异函数的空间随机森林插值方法

doi: 10.12357/cjea.20210628
基金项目: 国家重点研发计划项目(2016YFD0800301)资助
详细信息
    作者简介:

    王铭鑫, 主要从事空间统计分析等研究。E-mail: 731553275@qq.com

    通讯作者:

    高秉博, 主要从事空间统计分析等研究。E-mail: gaobingbo@cau.edu.cn

  • 中图分类号: X53

A spatial random forest interpolation method with semi-variogram

Funds: The study was supported by the National Key Research and Development Program of China (2016YFD0800301).
More Information
  • 摘要: 土壤环境变量具有较强空间异质性, 为空间插值精度的提升带来了困难, 仅基于空间相关性和空间异质性的空间插值方法难以获得较高的插值精度。机器学习方法能够融合多维辅助变量的信息, 提高土壤属性的插值精度, 但是不能有效融合空间位置关系信息进一步改善插值精度。本文基于随机森林空间预测框架, 将空间半变异函数与随机森林算法融合, 提出了融合半变异函数的空间随机森林插值方法。应用所提出的方法对湖南省湘潭县土壤重金属数据进行空间插值, 并与随机森林方法、基于距离的随机森林空间预测方法、普通克里金方法和回归克里金方法进行对比, 检验了所提出方法的插值精度。结果表明, 融合半变异函数的空间随机森林插值方法相较于传统克里金方法精度提升10%以上, 相较于新型机器学习空间插值方法精度提升5%以上, 同时基于半变异函数的空间随机森林插值方法的插值制图结果具有更加合理的空间分布和丰富的细节信息。因此, 融合半变异函数的空间随机森林插值方法能够有效结合辅助变量信息与空间位置关系信息, 有效提高土壤环境变量插值精度。
  • 图  1  研究区域及样点分布

    Figure  1.  Study area and sampling point

    图  2  研究区辅助变量土壤类型(a)、土地利用类型(b)、海拔(c)和坡度(d)的映射

    Figure  2.  Mapping of auxiliary variables in the study area. a: soil type; b: land use type; c: altitude; d: slope.

    图  3  研究区Cr含量半变异函数

    Figure  3.  Variation function of Cr content in the study area

    图  4  不同插值方法交叉验证结果的平均绝对误差(MAE)和均方根误差(RMSE)

    RF: 随机森林法; RFsp: 随机森林空间预测框架; SRFsei: 融合半变异函数的空间随机森林插值法; UK: 泛克里金; OK: 普通克里金。

    Figure  4.  Mean absolute error (MAE) and root mean square error (RMSE) of cross-validation results of different interpolation methods

    RF: random forest; RFsp: random forest for spatial predictions framework; SRFsei: spatial random forest with semi-variogram interpolation; UK: universal Kriging; OK: ordinary Kriging.

    图  5  融合半变异函数的空间随机森林插值法的插值结果图(a)与误差标准差图(b)

    Figure  5.  Interpolation results (a) and standard deviation variance of error (b) of spatial random forest with semi-variogram interpolation

    图  6  不同插值方法的插值结果图(a)及误差标准差图(b)

    RF: 随机森林法; RFsp: 随机森林空间预测框架; OK: 普通克里金; UK: 泛克里金。

    Figure  6.  Interpolation results (a) and standard deviation variance of error (b) of different interpolation methods

    RF: random forest; RFsp: random forest for spatial predictions framework; OK: ordinary Kriging; UK: universal Kriging.

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出版历程
  • 收稿日期:  2021-09-14
  • 录用日期:  2021-11-26
  • 网络出版日期:  2021-11-30
  • 刊出日期:  2022-03-07

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