摘要
随着环境信息获取技术的飞速发展,水质监测数据逐渐呈现出高时间分辨率的特点.针对传统方法在模拟高频检测数据时的不足,本文基于随机森林建立了河流氮、磷逐日浓度预测模型,识别了影响辽河干流马虎山断面氮、磷浓度变化的主要因素,并预测了未来不同情景下氮、磷浓度变化特征.结果表明:(1)马虎山断面氮、磷浓度变化的主要影响因素是上游珠尔山断面的来水水质和本断面的流量;(2)随机森林模型在高时间分辨率的水质指标模拟中具有误差低和拟合优度高的特点,其中,TN浓度预测模型的RMSE为0.40 mg·L^(-1),R^(2)为0.95,TP浓度预测模型的RMSE为0.01 mg·L^(-1),R^(2)为0.96;(3)在不同水文、污染控制和来水水质的变化情景下,未来马虎山断面氮、磷浓度变化主要取决于上游来水水质,加强全流域营养盐控制是确保断面水质稳定达标的重要基础.
With the rapid development of environmental information acquisition technology,the resolution of water quality monitoring data becomes much higher.To overcome shortcomings of traditional methods in simulating high frequency monitoring data,we developed a random forest model to predict daily nitrogen and phosphorus concentrations in rivers.Then,we identified key factors impacting nitrogen and phosphorus concentrations of Mahushan section of the main stream of the Liaohe River.Next,we predicted dynamics of nitrogen and phosphorus concentrations under different future scenarios.The results shown that key factors impacting nitrogen and phosphorus concentrations of Mahushan section of the main stream of the Liaohe River were water quality of the upstream Zhuershan section and the flow of this section.We also found the that random forest model had low errors and a high goodness of fit in the simulation of high temporal resolution water quality indicators.The RMSE and R^(2)for total nitrogen were 0.40 mg·L^(-1)and 0.95,while those for total phosphorus were 0.01 mg·L^(-1)and 0.96,respectively.We thereby concluded that under different future scenarios of hydrology,pollution control,and upstream water quality,the dynamics of nitrogen and phosphorus concentrations at the Mahushan section will mainly depend on the upstream water quality.Strengthening nutrient control in the entire watershed is the basis to ensure the stable compliance of water quality of this section.
作者
杨宇锋
武暕
王璐
郭杨
惠婷婷
高伟
张远
YANG Yufeng;WU Jian;WANG Lu;GUO Yang;HUI Tingting;GAO Wei;ZHANG Yuan(Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds,School of Ecology,Environment and Resources,Guangdong University of Technology,Guangzhou 510006;Monitoring Center of Ecology and Environment of Liaoning Province,Shenyang 110161)
出处
《环境科学学报》
CAS
CSCD
北大核心
2022年第12期384-391,共8页
Acta Scientiae Circumstantiae
基金
广东省基础与应用基础研究基金面上项目(No.2022A1515010789)
“珠江人才计划”引进创新创业团队资助项目(No.2019ZT08L213)。
关键词
营养盐
氮、磷
水质预测
机器学习
自动监测站
高时间分辨率
辽河
nutrient
nitrogen and phosphorus
water quality prediction
machine learning
auto-monitoring station
high time resolution
Liao River
作者简介
杨宇锋(1999-),男,E-mail:2112124067@mail2.gdut.edu.cn;责任作者:惠婷婷,E-mail:huitingting1020@126.com。
