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Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

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  • Shiqi Tian

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Shijie Wang

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Xiaoyong Bai

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    CAS Center for Excellence in Quaternary Science and Global Change, Xi’an 710061, Shanxi Province, China
    Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University, Guiyang 550018, Guizhou Province, China)

  • Dequan Zhou

    (School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, Guizhou Province, China)

  • Guangjie Luo

    (Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, Guizhou Education University, Guiyang 550018, Guizhou Province, China)

  • Jinfeng Wang

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Mingming Wang

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Qian Lu

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Yujie Yang

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Zeyin Hu

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Chaojun Li

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    School of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

  • Yuanhong Deng

    (State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, Guizhou Province, China
    Puding Karst Ecosystem Observation and Research Station, Chinese Academy of Sciences, Puding 562100, Guizhou Province, China)

Abstract

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.

Suggested Citation

  • Shiqi Tian & Shijie Wang & Xiaoyong Bai & Dequan Zhou & Guangjie Luo & Jinfeng Wang & Mingming Wang & Qian Lu & Yujie Yang & Zeyin Hu & Chaojun Li & Yuanhong Deng, 2019. "Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm," Sustainability, MDPI, vol. 11(11), pages 1-21, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:11:p:3197-:d:238112
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    1. Emmanuel Obeng-Gyasi, 2018. "Hepatobiliary Related Outcomes in US Adults Exposed to Lead," 2018 Stata Conference 81, Stata Users Group.
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    Cited by:

    1. Yun Xue & Bin Zou & Yimin Wen & Yulong Tu & Liwei Xiong, 2020. "Hyperspectral Inversion of Chromium Content in Soil Using Support Vector Machine Combined with Lab and Field Spectra," Sustainability, MDPI, vol. 12(11), pages 1-16, May.

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