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Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Author

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  • Shuaiwei Shi

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

  • Meiyi Hou

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

  • Zifan Gu

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

  • Ce Jiang

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

  • Weiqiang Zhang

    (School of Economics and Management, China University of Geosciences, Beijing 100083, China)

  • Mengyang Hou

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

  • Chenxi Li

    (School of Public Administration, Xi’an University of Architecture and Technology, Xi’an 710311, China)

  • Zenglei Xi

    (School of Economics, Hebei University, Baoding 071000, China
    Research Center for Resource Utilization and Environmental Protection, Hebei University, Baoding 071000, China)

Abstract

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

Suggested Citation

  • Shuaiwei Shi & Meiyi Hou & Zifan Gu & Ce Jiang & Weiqiang Zhang & Mengyang Hou & Chenxi Li & Zenglei Xi, 2022. "Estimation of Heavy Metal Content in Soil Based on Machine Learning Models," Land, MDPI, vol. 11(7), pages 1-19, July.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1037-:d:858479
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    References listed on IDEAS

    as
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