IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i7p1037-d858479.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/7/1037/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/11/7/1037/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Xunpeng & Wang, Keying & Cheong, Tsun Se & Zhang, Hongwu, 2020. "Prioritizing driving factors of household carbon emissions: An application of the LASSO model with survey data," Energy Economics, Elsevier, vol. 92(C).
    2. Ahmed Saleh & Yehia H. Dawood & Ahmed Gad, 2022. "Assessment of Potentially Toxic Elements’ Contamination in the Soil of Greater Cairo, Egypt Using Geochemical and Magnetic Attributes," Land, MDPI, vol. 11(3), pages 1-19, February.
    3. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    4. Shuangshuang Fan & Yichao Li & William Mbanyele & Xiufeng Lai, 2025. "Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 65(3), pages 1231-1264, March.
    5. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    6. Alberti, Federica & Mantilla, César, 2020. "Provision of noxious facilities using a market-like mechanism: A simple implementation in the lab," Working papers 35, Red Investigadores de Economía.
    7. Yikalo H. Araya & Tarmo K. Remmel & Ajith H. Perera, 2016. "What governs the presence of residual vegetation in boreal wildfires?," Journal of Geographical Systems, Springer, vol. 18(2), pages 159-181, April.
    8. Feng, Wei & Sun, Shujun & Yuan, Hang, 2023. "Research on the efficiency of factor allocation in the pilot free trade zones," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 727-745.
    9. Md Mohsan Khudri & Kang Keun Rhee & Mohammad Shabbir Hasan & Karar Zunaid Ahsan, 2023. "Predicting nutritional status for women of childbearing age from their economic, health, and demographic features: A supervised machine learning approach," PLOS ONE, Public Library of Science, vol. 18(5), pages 1-31, May.
    10. Sandro Radovanovic & Boris Delibasic & Milija Suknovic & Dajana Matovic, 2019. "Where will the next ski injury occur? A system for visual and predictive analytics of ski injuries," Operational Research, Springer, vol. 19(4), pages 973-992, December.
    11. Vincenzo Bianco & Annalisa Marchitto & Federico Scarpa & Luca A. Tagliafico, 2020. "Forecasting Energy Consumption in the EU Residential Sector," IJERPH, MDPI, vol. 17(7), pages 1-15, March.
    12. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    13. Zhang, Guike & Gao, Zengan & Dong, June & Mei, Dexiang, 2023. "Machine learning approaches for constructing the national anti-money laundering index," Finance Research Letters, Elsevier, vol. 52(C).
    14. Sarah Mittlefehldt & Erin Bunting & Emily Huff & Joseph Welsh & Robert Goodwin, 2021. "New Methods for Assessing Sustainability of Wood-Burning Energy Facilities: Combining Historical and Spatial Approaches," Energies, MDPI, vol. 14(23), pages 1-18, November.
    15. Jiayi Zhou & Kangning Xiong & Qi Wang & Jiuhan Tang & Li Lin, 2022. "A Review of Ecological Assets and Ecological Products Supply: Implications for the Karst Rocky Desertification Control," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    16. Lee Anthony & Caron Francois & Doucet Arnaud & Holmes Chris, 2012. "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(2), pages 1-31, January.
    17. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2016. "The lasso for high dimensional regression with a possible change point," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 193-210, January.
    18. Hautsch, Nikolaus & Okhrin, Ostap & Ristig, Alexander, 2014. "Efficient iterative maximum likelihood estimation of high-parameterized time series models," SFB 649 Discussion Papers 2014-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Stefania Corsaro & Valentina De Simone & Zelda Marino & Salvatore Scognamiglio, 2022. "l 1 -Regularization in Portfolio Selection with Machine Learning," Mathematics, MDPI, vol. 10(4), pages 1-15, February.
    20. Bemah Ibrahim & Isaac Ahenkorah & Anthony Ewusi, 2022. "Explainable Risk Assessment of Rockbolts’ Failure in Underground Coal Mines Based on Categorical Gradient Boosting and SHapley Additive exPlanations (SHAP)," Sustainability, MDPI, vol. 14(19), pages 1-16, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:11:y:2022:i:7:p:1037-:d:858479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.