Application of Machine Learning Approaches to Predict Soil Element Background Concentration at Large Region Scale

Abstract

Soil element background concentration is foundational data for environmental quality assessment, contamination diagnosis, and sustainable land management. However, existing investigation-based methods are time-consuming and inefficient. The machine learning (ML) method has demonstrated excellent performance in predicting soil heavy metal concentration. In this study, based on the nine environmental variables of soil formation from 210 soil monitoring points, including elevation, pH, organic matter, soil type, parent material, plant cover, land use type, topography, and soil texture, decision tree (DT), random forest (RF), extreme gradient boosting (XGB), and support vector machine (SVM) models were used to predict the eleven soil element background concentrations. Among them, SVM and RF models could be used for an effective prediction of the background concentration of all soil heavy metals. Compared with the XGBoost and DT, the SVM for all heavy metals except for cadmium (Cd) and manganese (Mn) performs best. Although the key factors affecting background concentrations vary among different soil elements, organic matter, soil type, and altitude, they play a crucial role in the accurate prediction of soil element background concentration. This study provides simple and efficient ML models for predicting soil element background concentration at the large regional scale. The results of this study can be utilized to distinguish natural geochemical processes from human-induced pollution.