Prediction of phosphorus concentrations in shallow groundwater in intensive agricultural regions based on machine learning
文献类型： 外文期刊
作者： Yang, Heng 1 ; Wang, Panlei 2 ; Chen, Anqiang 2 ; Ye, Yuanhang 1 ; Chen, Qingfei 1 ; Cui, Rongyang 3 ; Zhang, Dan 1 ;
作者机构： 1.Yunnan Agr Univ, Coll Resource & Environm, Kunming 650201, Peoples R China
2.Yunnan Acad Agr Sci, Agr Environm & Resources Inst, Kunming 650201, Peoples R China
3.Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Surface Proc & Ecol Regulat, Minist Water Conservancy, Chengdu 610041, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
关键词： Shallow groundwater; Phosphorus; Machine learning; Intensive agricultural region
期刊名称：CHEMOSPHERE （ 影响因子：8.8； 五年影响因子：8.3 ）
ISSN： 0045-6535
年卷期： 2023 年 313 卷
页码：
收录情况： SCI
摘要： Excessive accumulation of phosphorus in soil profiles has become the main source of phosphorus in groundwater due to the application of phosphorus fertilizers in intensive agricultural regions (IARs). Elevated phosphorus concentrations in groundwater have become a global phenomenon, which places enormous pressure on the safe use of water resources and the safety of the aquatic environment. Currently, the prediction of pollutant concentrations in groundwater mainly focuses on nitrate nitrogen, while research on phosphorus prediction is limited. Taking the IARs approximately 8 plateau lakes in the Yunnan-Guizhou Plateau as an example, 570 shallow groundwater samples and 28 predictor variables were collected and measured, and a machine learning approach was used to predict phosphorus concentrations in groundwater. The performance of three machine learning algorithms and different sets of variables for predicting phosphorus concentrations in shallow groundwater was evaluated. The results showed that after all variables were introduced into the model, the R2, RMSE and MAE of support vector machine (SVM), random forest (RF) and neural network (NN) were 0.52-0.60, 0.101-0.108 and 0.074-0.081, respectively. Among them, the SVM model had the best prediction effect. The clay content and water-soluble phosphorus in soil and soluble organic carbon in groundwater had a high contribution to the prediction accuracy of the model. The prediction accuracy of the model with reduced number of variables
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