IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v15y2017i1p34-d124401.html
   My bibliography  Save this article

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

Author

Listed:
  • Zhenyi Jia

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Shenglu Zhou

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Quanlong Su

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Haomin Yi

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

  • Junxiao Wang

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China)

Abstract

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

Suggested Citation

  • Zhenyi Jia & Shenglu Zhou & Quanlong Su & Haomin Yi & Junxiao Wang, 2017. "Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network," IJERPH, MDPI, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2017:i:1:p:34-:d:124401
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/15/1/34/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/15/1/34/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Swatantra R. Kethireddy & Paul B. Tchounwou & Hafiz A. Ahmad & Anjaneyulu Yerramilli & John H. Young, 2014. "Geospatial Interpolation and Mapping of Tropospheric Ozone Pollution Using Geostatistics," IJERPH, MDPI, vol. 11(1), pages 1-18, January.
    2. Chunhui Li & Chuanhua Yu, 2013. "Performance Evaluation of Public Non-Profit Hospitals Using a BP Artificial Neural Network: The Case of Hubei Province in China," IJERPH, MDPI, vol. 10(8), pages 1-15, August.
    3. Kudakwashe K. Shamuyarira & Jabulani R. Gumbo, 2014. "Assessment of Heavy Metals in Municipal Sewage Sludge: A Case Study of Limpopo Province, South Africa," IJERPH, MDPI, vol. 11(3), pages 1-11, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ching-Ping Liang & Chi-Chien Sun & Heejun Suk & Sheng-Wei Wang & Jui-Sheng Chen, 2021. "A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain," IJERPH, MDPI, vol. 18(21), pages 1-15, October.

    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. Baoling Duan & Qiang Feng, 2022. "Risk Assessment and Potential Analysis of the Agricultural Use of Sewage Sludge in Central Shanxi Province," IJERPH, MDPI, vol. 19(7), pages 1-12, April.
    2. Lian Chen & Genmei Wang & Shaohua Wu & Zhen Xia & Zhenang Cui & Chunhui Wang & Shenglu Zhou, 2019. "Heavy Metals in Agricultural Soils of the Lihe River Watershed, East China: Spatial Distribution, Ecological Risk, and Pollution Source," IJERPH, MDPI, vol. 16(12), pages 1-17, June.
    3. Baoling Duan & Fenwu Liu & Wuping Zhang & Haixia Zheng & Qiang Zhang & Xiaomei Li & Yushan Bu, 2015. "Evaluation and Source Apportionment of Heavy Metals (HMs) in Sewage Sludge of Municipal Wastewater Treatment Plants (WWTPs) in Shanxi, China," IJERPH, MDPI, vol. 12(12), pages 1-12, December.
    4. Piotr Boniecki & Małgorzata Idzior-Haufa & Agnieszka A. Pilarska & Krzysztof Pilarski & Alicja Kolasa-Wiecek, 2019. "Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm," IJERPH, MDPI, vol. 16(18), pages 1-9, September.
    5. Yan Li & Hua Yu & Siqian Zheng & Yang Miao & Shi Yin & Peng Li & Ying Bian, 2016. "Direct Quantification of Rare Earth Elements Concentrations in Urine of Workers Manufacturing Cerium, Lanthanum Oxide Ultrafine and Nanoparticles by a Developed and Validated ICP-MS," IJERPH, MDPI, vol. 13(3), pages 1-10, March.
    6. Mohammad Maleki & Kevin Soria, 2020. "Multivariate Geostatistical Modeling and Risk Analysis of Beach Litter: A Case Study of Playa Blanca Beach, Chile," IJERPH, MDPI, vol. 17(22), pages 1-15, November.
    7. Małgorzata Czatzkowska & Izabela Wolak & Monika Harnisz & Ewa Korzeniewska, 2022. "Impact of Anthropogenic Activities on the Dissemination of ARGs in the Environment—A Review," IJERPH, MDPI, vol. 19(19), pages 1-29, October.

    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:jijerp:v:15:y:2017:i:1:p:34-:d:124401. 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.