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

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
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    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.

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