基于敏感变量选择和机器学习算法,覆盖的农田土壤盐度的倒置模型
Hong Ma1,2,3, Wenju Zhao1,3, Weicheng Duan1,3
1College of Energy and Power Engineering, Lanzhou University of Technology, Lanzhou, China.
PeerJ
|September 30, 2024
概括
将变量选择与机器学习相结合,可以显著提高土壤盐度监测的准确性. 灰色关系分析与支向量机器回归 (GRA-SVM) 证明在作物覆盖的农田中最有效.
科学领域:
- 农业遥感 农业遥感
- 土壤科学 土壤科学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的土壤盐度 (SSC) 监测对于大规模农业的有效灌管理至关重要.
- 无人机低空遥感为SSC监控提供了高空间和时间分辨率.
- 现有的模型往往缺乏综合评估的变量选择方法与机器学习算法相结合.
研究的目的:
- 研究将不同的变量选择方法与用于土壤盐度逆转的机器学习算法相结合的有效性.
- 确定可变选择和机器学习的最佳组合,以在作物覆盖的农田中进行准确的SSC监测.
- 用R2,RMSE和RPD等指标来评估各种模型的性能.
主要方法:
- 从无人机多谱数据中提取的光谱指数.
- 采用了四种变量选择方法:皮尔森相关系数 (PCC),灰色关系分析 (GRA),可变投影重要性 (VIP) 和支持向量机-递归特征消除 (SVM-RFE).
- 开发了20个土壤盐度逆转模型,使用支持向量机回归 (SVM),反向传播神经网络 (BPNN),极端学习机 (ELM) 和随机森林 (RF) 算法,比较选和未选的变量.
主要成果:
- 变量选择与机器学习相结合,显著提高了土壤盐度逆转的准确性.
- 灰色关系分析 (GRA) 适用于SVM,BPNN和ELM,而PCC最适合RF.
- 在覆盖的农田中,GRA-SVM模型获得了最高的精度 (Rv2=0.8888,RMSEv=0.1780,RPD=1.8115).
结论:
- 将可变选择方法与机器学习算法相结合,是改善基于遥感的土壤盐度逆转的高效方法.
- 这项研究提供了一个强大的方法论,用于在作物覆盖的农田中准确和及时获取SSC信息.
- GRA-SVM模型为干旱绿洲灌区的土壤盐度绘制和管理提供了可靠的解决方案.
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