上海国土资源2024，Vol.45Issue(2)：41-47,7.DOI:10.3969/j.issn.2095-1329.2024.02.009
利用改进XGBoost模型预测和分析湿地潜流带地下水中硝态氮含量
Using an improved XGBoost model to predict and analyze nitrate nitrogen content in groundwater of wetland hyporheic zones
摘要
Abstract
The hyporheic zone in wetlands is an important area for nitrogen cycling in groundwater.The hyporheic zone of Dongting Lake wetlands is taking as the research object,this study explores the influencing factors and mechanisms of nitrogen migration and transformation in groundwater.4 profiles and a total of 16 monitoring wells were set up in the wetland at the entrance of the Xiangjiang River,and groundwater samples were tested and analyzed for one hydrological year.The selected characteristic parameters for the study include redox potential(Eh),dissolved oxygen(DO),water temperature(T),groundwater level(H)and burial depth,pH,and dissolved organic carbon(DOC).An XGBoost machine learning model is established to predict the relative concentration of nitrate nitrogen.The optimal XGBoost prediction model(BO XGBoost)is obtained by using Bayesian Optimization(BO),Sparrow Search Algorithm(SSA),and Particle Swarm Optimization(PSO)algorithms to optimize the hyperparameters of the XGBoost prediction model.Based on this,the SHAP(Shapley Additive exPlans)method is used to analyze the interpretability of the BO-XGBoost model.The research results indicate that the BO-XGBoost model has the best performance,with determination coefficients exceeding 0.90 in both the training and testing sets.The interpretability analysis results and correlation analysis reveal that the impact of factors such as Eh,DO,T,H,pH,and DOC on the nitrate nitrogen content in groundwater in wetland hyporheic zone gradually decreases.关键词
湿地潜流带/硝态氮/机器学习/XGBoost/SHAPKey words
wetland hyporheic zone/nitrate nitrogen/machine learning/XGBoost/Shapley Additive exPlans分类
天文与地球科学引用本文复制引用
周念清,夏明锐,陆帅帅,郭梦申,王在艾,赵文刚..利用改进XGBoost模型预测和分析湿地潜流带地下水中硝态氮含量[J].上海国土资源,2024,45(2):41-47,7.基金项目
国家自然科学基金项目(42077176 （）
42272291 （）
42242202) （）
