Study based on machine learning on the impact of fluoride-contaminated farmland remediation on farmers’ income structure and rural economic revitalization

This study addresses the specific question of how the remediation of fluoride-contaminated farmland can quantify its impact on farmers’ income structure and its transmission to rural economic revitalization. To solve this problem, firstly, multi-source data were integrated to construct governance characteristic variables; then, the Light Gradient Boosting Machine (LightGBM) model was used to predict farmers’ multidimensional income, and SHapley Additive explanations (SHAP) values were employed t