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

Abstract

● 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.

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.

Graphical abstract

Keywords

Soil contamination / Machine learning / Prediction / Spatial distribution

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