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

Journal: Chemosphere
PMID: 38944354

Abstract

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 R 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.

Authors

  • Sangar Khan
    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China; Donghai Institute, Ningbo University, Ningbo, 315211, China; Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
  • Huimin Gao
    Peking University Sixth Hospital, Beijing, China.
  • Paul Milham
    Hawkesbury Institute for the Environment, University of Western Sydney, LB 1797, Penrith, New South Wales, 2751, Australia.
  • Kamel Mohamed Eltohamy
    College of Environmental Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Water Relations and Field Irrigation Department, Agricultural and Biological Research Division, National Research Centre, 12622, Cairo, Egypt.
  • Habib Ullah
    Innovation Center of Yangtze River Delta, Zhejiang University, Zhejiang, 311400, China.
  • Hongli Mu
    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China; Donghai Institute, Ningbo University, Ningbo, 315211, China; Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
  • Meixiang Gao
    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China; Donghai Institute, Ningbo University, Ningbo, 315211, China; Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China.
  • Xiaodong Yang
    Clinical and Research Center for Infectious Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China.
  • Yasir Hamid
    College of Environmental Resource Sciences, Zhejiang University, Hangzhou, 310058, China.
  • Peter S Hooda
    Faculty of Engineering, Computing and the Environment, Kingston University London, UK.
  • Sabry M Shaheen
    University of Wuppertal, School of Architecture and Civil Engineering, Laboratory of Soil Groundwater Management, Pauluskirchstraße 7, 42285, Wuppertal, Germany; King Abdulaziz University, Faculty of Environmental Sciences, Department of Agriculture, 21589 Jeddah, Saudi Arabia; University of Kafrelsheikh, Faculty of Agriculture, Department of Soil and Water Sciences, 33516, Kafr El-Sheikh, Egypt.
  • Naicheng Wu
    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China; Donghai Institute, Ningbo University, Ningbo, 315211, China; Zhejiang Collaborative Innovation Center for Land and Marine Spatial Utilization and Governance Research, Ningbo University, Ningbo, 315211, China; Institute of Hydraulic and Ocean Engineering, Ningbo, 315211, China. Electronic address: naichengwu88@gmail.com.

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