河北水利电力学院学报2026，Vol.36Issue(2)：1-8,8.DOI:10.16046/j.cnki.issn2096-5680.2026.02.001
基于极端梯度提升树模型的固安县温泉园区调蓄水池水质监测
Water Quality Monitoring of Regulating Reservoir in Gu'an Hot Spring Park Based on Extreme Gradient Boosting Tree Model
摘要
Abstract
Satellite remote sensing technology,leveraging its high spatial resolution and short revisit cycle,offers an effective approach for water quality monitoring.This study constructed a machine learning model based on Extreme Gradient Boosting(XGBoost)researching on reservoirs in Gu'an Hot Spring Park.Using domestic remote sensing images from the Gaofen(GF),Ziyuan(ZY),Beijing(BJ),and Jilin(JL)satellite series,the model retrieved key water quality parameters including chlorophyll-a,pH,am-monia nitrogen,and dissolved oxygen.The reflectance was derived from remote sensing images after pre-processing steps such as radiometric calibration and atmospheric correction,while synchronized measured data were obtained from buoy-based online monitoring stations.Band combinations with high correlation(coefficient>0.6)selected through Pearson correlation analysis were used as model inputs.The dataset was split into training,validation,and test sets in a 7∶1∶2 ratio,and model performance was evaluated u-sing R2,RMSE,and MAPE.The results demonstrated that the XGBoost model outperformed compara-tive models—Random Forest,AdaBoost,Deep Neural Networks,Convolutional Neural Networks,and Ordinary Linear Regression—in retrieving all four water quality parameters,exhibiting excellent fitting ca-pability on the training set and strong generalization ability on the test set.High-spatial-resolution water quality distribution maps generated by the model revealed a spatial pattern of"poor at the edges and good in the center"within the study area,with water quality in the southern reservoir being superior to that in the northern reservoir.This research provides effective technical support for precise monitoring and man-agement of water quality in small and medium-sized water bodies.关键词
遥感影像/水质监测/XGBoost模型/反演/空间分布Key words
remote sensing images/water quality monitoring/XGBoost model/inversion/spatial dis-tribution分类
建筑与水利引用本文复制引用
魏喜远,孙敬涛,刘世璞,门旭净,刘倩..基于极端梯度提升树模型的固安县温泉园区调蓄水池水质监测[J].河北水利电力学院学报,2026,36(2):1-8,8.基金项目
河北省水利工程局集团固安县温泉园区调蓄水池水质监测预警研究项目(2024-02-CX-009) （2024-02-CX-009）
河北省水利工程局集团固安县温泉园区调蓄水池生态服务价值评估项目(2024-02-CX-013) （2024-02-CX-013）
河北省高等学校科学研究项目(QN2025397) （QN2025397）
