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文章摘要
基于随机森林与神经网络的高光谱土壤重金属Zn含量反演
Hyperspectral Inversion of Heavy Metal Zn Content in Soil Based on Random Forest and Neural Network
  
DOI:
中文关键词: Zn含量反演  高光谱波段  双层随机森林  BP神经网络  土壤
英文关键词: Zn content inversion  Hyperspectral band  Double-layer random forest  BP neural networks  Soils
基金项目:国家自然科学青年基金资助项目(41802249);河北省教育厅科学技术研究基金资助项目(ZD2021017);河北省自然科学青年基金资助项目(D2019403165)
作者单位
肖树群 河北地质大学资源与环境工程研究所河北地质大学信息工程学院 
汪海城 河北地质大学资源与环境工程研究所河北地质大学河北省战略性关键矿产资源重点实验室 
袁兆宪 河北地质大学资源与环境工程研究所河北地质大学河北省战略性关键矿产资源重点实验室 
张生元 河北地质大学资源与环境工程研究所河北地质大学河北省战略性关键矿产资源重点实验室 
黄波 河北省水文工程地质勘查院(河北省遥感中心) 
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中文摘要:
      在河北省保定市白洋淀区域采集115个土壤样品进行重金属含量分析和室内光谱测量,分别将BP神经网络、随机森林、决策树、多元线性回归、K近邻回归、AdaBoost回归和偏最小二乘回归法应用于全部原始波谱数据和基于双层随机森林选择后的波段数据。结果表明,基于原始波谱数据的土壤重金属Zn元素含量的反演模型精度较低,而通过双层随机森林选择出光谱数据中与土壤重金属Zn信息相关的波段,减轻了网络模型的过拟合问题,提高了模型预测精度;与其他模型比较,结合双层随机森林和BP神经网络构建的反演模型对研究区土壤重金属Zn含量预测效果最佳。
英文摘要:
      A total of 115 soil samples were collected from Baiyangdian District of Baoding, Hebei Province for heavy metal content analysis and indoor spectral measurement. BP neural network, random forest, decision tree, multiple linear regression, K-nearest neighbor regression, AdaBoost regression and partial least squares regression were applied to all original spectral data and band data selected based on double-layer random forest. The results showed that the inversion model of the content of heavy metal Zn in soil based on original spectral data had a low accuracy, and the bands related to the information of heavy metal Zn in soil were selected from spectral data by double-layer random forest, which alleviated the overfitting problem of the network model and improved the model prediction accuracy. Compared with other models, the inversion model constructed by combining double-layer random forest and BP neural network had the best prediction effect on the content of heavy metal Zn in soil of the study area.
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