Improving the spatial prediction of soil Zn by converting outliers into soft data for BME method

  • Chu-tian Zhang
  • Yong YangEmail author
Original Paper


Understanding the spatial patterns of heavy metals is important for the protection and remediation of urban soil. Considering that the conventional Geostatistical methods, such as ordinary kriging (OK), are sensitive to dataset outliers, this study converted the identified outliers into a discrete probability density function (PDF). Then, the PDF was used as soft data in the Bayesian maximum entropy (BME) framework to perform a spatial prediction of soil Zn contents in Wuhan City, Central China. By using OK as the reference method, the BME framework was found to produce an overall further accurate prediction, and the PDF of BME predictions was further informative and close to the observed Zn concentrations. An improved BME performance can be expected if soft data with high quality are provided. The BME is a promising method in environmental science, where the so-called outliers that probably carry important information are common.


Bayesian maximum entropy Soil Zn contents Outliers Discrete probability density function Soft data 



This work was supported by the National Natural Science Foundation of China (Grant Nos. 41701239 and 41671217) and the Fundamental Research Funds for the Central Universities (No. 2452016190).

Supplementary material

477_2018_1641_MOESM1_ESM.docx (687 kb)
Supplementary material 1 (DOCX 687 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Natural Resources and EnvironmentNorthwest A&F UniversityYanglingChina
  2. 2.College of Resources and EnvironmentHuazhong Agricultural UniversityWuhanChina
  3. 3.Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River)Ministry of AgricultureWuhanChina

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