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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

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    1. Peters, Jan & Baets, Bernard De & Verhoest, Niko E.C. & Samson, Roeland & Degroeve, Sven & Becker, Piet De & Huybrechts, Willy, 2007. "Random forests as a tool for ecohydrological distribution modelling," Ecological Modelling, Elsevier, vol. 207(2), pages 304-318.
    2. Yang Yu & Yue Ling & Yunzhao Li & Zhenbo Lv & Zhaohong Du & Bo Guan & Zhikang Wang & Xuehong Wang & Jisong Yang & Junbao Yu, 2022. "Distribution and Influencing Factors of Metals in Surface Soil from the Yellow River Delta, China," Land, MDPI, vol. 11(4), pages 1-17, April.
    3. Chenxi Li & Kening Wu & Xiangyu Gao, 2020. "Manufacturing industry agglomeration and spatial clustering: Evidence from Hebei Province, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 2941-2965, April.
    4. Huihui Zhao & Peijia Liu & Baojin Qiao & Kening Wu, 2021. "The Spatial Distribution and Prediction of Soil Heavy Metals Based on Measured Samples and Multi-Spectral Images in Tai Lake of China," Land, MDPI, vol. 10(11), pages 1-13, November.
    5. Fang Xia & Youwei Zhu & Bifeng Hu & Xueyao Chen & Hongyi Li & Kejian Shi & Liuchang Xu, 2021. "Pollution Characteristics, Spatial Patterns, and Sources of Toxic Elements in Soils from a Typical Industrial City of Eastern China," Land, MDPI, vol. 10(11), pages 1-20, October.
    6. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    7. Huisheng Yu & Jun Yang & Dongqi Sun & Tong Li & Yanjun Liu, 2022. "Spatial Responses of Ecosystem Service Value during the Development of Urban Agglomerations," Land, MDPI, vol. 11(2), pages 1-12, January.
    8. Robert Tibshirani, 2011. "Regression shrinkage and selection via the lasso: a retrospective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 273-282, June.
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