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Spatial prediction of soil contamination based on machine learning: a review

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Abstract

Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (No. 2018YFC1800100); the National Natural Science Foundation of China (No. 42277475).

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Authors and Affiliations

  1. Institute of Geographic Sciences & Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Yang Zhang, Mei Lei, Kai Li & Tienan Ju

  2. Sino-Danish College, University of Chinese Academy of Sciences, Beijing, 100190, China

    Yang Zhang & Mei Lei

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  1. Yang Zhang
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Correspondence to Mei Lei.

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Highlights

• A review of machine learning (ML) for spatial prediction of soil contamination.

• ML have achieved significant breakthroughs for soil contamination prediction.

• A structured guideline for using ML in soil contamination is proposed.

• The guideline includes variable selection, model evaluation, and interpretation.

Special Issue—Artificial Intelligence/Machine Learning on Environmental Science & Engineering (Responsible Editors: Yongsheng Chen, Xiaonan Wang, Joe F. Bozeman III & Shouliang Yi)

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Zhang, Y., Lei, M., Li, K. et al. Spatial prediction of soil contamination based on machine learning: a review. Front. Environ. Sci. Eng. 17, 93 (2023). https://doi.org/10.1007/s11783-023-1693-1

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