Predicting the governing factors for the release of colloidal phosphorus using machine learning

Predicting the parameters that influence colloidal phosphorus (CP) release from soils under different land uses is critical for managing the impact on water quality. Traditional modeling approaches, such as linear regression, may fail to represent the intricate relationships that exist between soil qualities and environmental influences. Therefore, in this study, we investigated the major determinants of CP release from different land use/types such as farmland, desert, forest soils, and rivers. The study utilizes the structural equation model (SEM), multiple linear regression (MLR), and three machine learning (ML) models (Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGBoost)) to predict the release of CP from different soils by using soil iron (Fe), aluminum (Al), calcium (Ca), pH, total organic carbon (TOC) and precipitation as independent variables. Results show that colloidal-cations (Fe, Al, Ca) and colloidal-TOC strongly influence CP release, while bioclimatic variables (precipitation) and pH have weaker effects. XGBoost outperforms the other models with an R² of 0.94 and RMSE of 0.09. SHapley Additive Explanations described the outcomes since XGBoost is accurate. The relative relevance ranking indicated that colloidal TOC had the highest ranking in predicting CP. This was supported by the analysis of partial dependence plots, which showed that an increase in colloidal TOC increased soil CP release. According to our research, the SHAP XGBoost model provides significant information that can help determine the variables that considerably influence CP contents as compared to RF, SVM, and MLR.