A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine learning models

= 0.61). The Shapley additive explanations suggested significant differences in the driving factors and their contributions to prediction models for each heavy metal. Climate variables were potentially valuable predictors of heavy metal content. The visualized spatial distribution of Pollution Load Index showed that 79.8 % of the study area was moderately polluted and the remaining 20.2 % was in a severe polluted state. The soil pollution state worsened from the west to the east of the study area. These findings provide valuable information on effective soil pollution control and soil conservation efforts.