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基于GA-SVM的GNSS-IR土壤湿度反演方法

孙波 梁勇 汉牟田 杨磊 荆丽丽 俞永庆

孙波, 梁勇, 汉牟田, 等 . 基于GA-SVM的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2019, 45(3): 486-492. doi: 10.13700/j.bh.1001-5965.2018.0417
引用本文: 孙波, 梁勇, 汉牟田, 等 . 基于GA-SVM的GNSS-IR土壤湿度反演方法[J]. 北京航空航天大学学报, 2019, 45(3): 486-492. doi: 10.13700/j.bh.1001-5965.2018.0417
SUN Bo, LIANG Yong, HAN Mutian, et al. GNSS-IR soil moisture inversion method based on GA-SVM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 486-492. doi: 10.13700/j.bh.1001-5965.2018.0417(in Chinese)
Citation: SUN Bo, LIANG Yong, HAN Mutian, et al. GNSS-IR soil moisture inversion method based on GA-SVM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 486-492. doi: 10.13700/j.bh.1001-5965.2018.0417(in Chinese)

基于GA-SVM的GNSS-IR土壤湿度反演方法

doi: 10.13700/j.bh.1001-5965.2018.0417
基金项目: 

国家重点研发计划 2016YFC0803104

北航北斗技术成果转化及产业化资金资助项目 BARI1709

山东农业大学一流学科资金 XXXY201703

山东农业大学重点培育学科国家自然科学基金申报项目资助计划 

金华市科技特派员项目 20180109151645582

详细信息
    作者简介:

    孙波  男, 博士研究生。主要研究方向:卫星导航及其应用

    梁勇  男, 博士, 教授, 博士生导师。主要研究方向:数字农业

    通讯作者:

    梁勇, E-mail:yongl@sdau.edu.cn

  • 中图分类号: P237; TB553

GNSS-IR soil moisture inversion method based on GA-SVM

Funds: 

National Key R & D Program of China 2016YFC0803104

Grant of Beihang University BeiDou Technology Transformation and Industrialization BARI1709

Shandong Agricultural University Funding of First-class Disciplines XXXY201703

Shandong Agricultural University Key Cultivation Discipline Funding for NSFC Proposers 

Jinhua Science and Technology Correspondent Project 20180109151645582

More Information
  • 摘要:

    针对提高大范围土壤湿度测量精度的问题,研究了土壤湿度的全球卫星导航系统干涉测量法(GNSS-IR),提出了一种基于支持向量机(SVM)的土壤湿度反演模型,利用遗传算法(GA)的自动寻优功能寻找SVM的最佳参数。结果表明,GA-SVM模型在测试集上得到的土壤湿度反演值与实测值的平均绝对百分比误差(MAPE)仅为0.69%,最大相对误差(MRE)为1.22%,线性回归方程决定系数达到了0.956 9。进一步与统计回归、粒子群优化的SVM模型(PSO-SVM)及反向传播(BP)神经网络方法进行对比,结果说明:在样本数目有限的情况下,GA-SVM方法更适用于土壤湿度的GNSS-IR技术反演,且反演精度较高,泛化性能良好。

     

  • 图 1  干涉场景

    Figure 1.  Scenario of interference

    图 2  上升段信噪比分析

    Figure 2.  Ascending SNR analysis

    图 3  基于GA-SVM的GNSS-IR土壤湿度反演模型

    Figure 3.  GNSS-IR soil moisture inversion model based on GA-SVM

    图 4  GA-SVM进化代数曲线

    Figure 4.  Evolution algebra curves of GA-SVM

    图 5  GA-SVM土壤湿度反演模型结果分析

    Figure 5.  Result analysis of GA-SVM soil moisture inversion model

    表  1  不同土壤湿度反演模型结果比较

    Table  1.   Result comparison of different soil moisture inversion models

    日期 实测值/(cm3·cm-3) GA-SVM PSO-SVM BP神经网络
    反演值/(cm3·cm-3) 绝对误差/(cm3·cm-3) 相对误差/% 反演值/(cm3·cm-3) 绝对误差/(cm3·cm-3) 相对误差/% 反演值/(cm3·cm-3) 绝对误差/(cm3·cm-3) 相对误差/%
    2014-03-10 25.83 25.76 -0.07 0.27 26.14 0.31 1.20 26.52 0.69 2.67
    2014-03-11 25.55 25.41 -0.14 0.55 26.18 0.63 2.47 26.95 1.40 5.48
    2014-03-12 25.21 25.10 -0.11 0.44 25.30 0.09 0.36 27.09 1.88 7.46
    2014-03-13 24.55 24.85 0.30 1.22 25.72 1.17 4.77 24.95 0.40 1.63
    2014-03-14 24.45 24.64 0.19 0.78 25.38 0.93 3.80 24.99 0.54 2.21
    2014-03-15 24.27 24.48 0.21 0.87 24.87 0.60 2.47 24.44 0.17 0.70
    2014-03-16 24.07 24.24 0.17 0.71 24.24 0.17 0.71 25.23 1.16 4.82
    2014-03-17 23.97 24.18 0.21 0.88 23.99 0.02 0.08 25.47 1.50 6.26
    2014-03-18 23.83 23.99 0.16 0.67 23.94 0.11 0.46 24.10 0.27 1.13
    2014-03-19 23.88 23.71 -0.17 0.71 24.62 0.74 3.10 24.66 0.78 3.27
    2014-03-20 23.62 23.58 -0.04 0.17 24.06 0.44 1.86 25.55 1.93 8.17
    2014-03-21 23.24 23.49 0.25 1.08 23.62 0.38 1.64 24.36 1.12 4.82
    下载: 导出CSV

    表  2  土壤湿度反演结果评价比较

    Table  2.   Comparison of soil moisture inversion result evaluation

    评价指标 计算方法 GA-SVM PSO-SVM BP神经网络
    平均绝对误差/(cm3·cm-3) 0.168 0.466 0.987
    最大相对误差/% 1.22 4.77 8.18
    均方根误差/(cm3·cm-3) 0.182 0.579 1.144
    平均绝对百分比误差/% 0.69 1.91 4.05
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-07-11
  • 录用日期:  2018-10-19
  • 刊出日期:  2019-03-20

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