ArticleInternational journal of environmental research and public health2017
Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.
Article in International journal of environmental research and public health, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- Trends in the impact of socioeconomic developments on polycyclic aromatic hydrocarbon concentrations in Dianchi Lake.Environmental science and pollution research international · 2022Article
- A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain.International journal of environmental research and public health · 2021Article
- Evaluation of Contamination and Ecological Risk of Heavy Metals Associated with Cement Production in Ewekoro, Southwest Nigeria.Journal of health & pollution · 2020Article
- Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sites.Environmental geochemistry and health · 2019Article
- Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging.Sensors (Basel, Switzerland) · 2018Article
- Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China.International journal of environmental research and public health · 2018Article
- Impact of the Coal Mining on the Spatial Distribution of Potentially Toxic Metals in Farmland Tillage Soil.Scientific reports · 2018Article
- Soil Pollution and Remediation.International journal of environmental research and public health · 2018Article
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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.
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