基于机器学习的卫星遥感水质富营养化评价——以合肥市环城河为例  

Evaluation of eutrophication by satellite remote sensing based on machine learning:A case study of Huancheng River in Hefei

作　　者：张勇 王慧[1,3] 朱传华 周浩 詹宇 李灿 肖逸凡 杨丽丽 刘佳奇 ZHANG Yong;WANG Hui;ZHU Chuanhua;ZHOU Hao;ZHAN Yu;LI Can;XIAO Yifan;YANG Lili;LIU Jiaqi(School of Environmental and Energy Engineering,Anhui Jianzhu University,Hefei 230601,China;Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse,Anhui Jianzhu University,Hefei 230601,China;Jingzhou Water Group Co.Ltd.,Jingzhou,Hubei 434000,China)

机构地区：[1]安徽建筑大学环境与能源工程学院,合肥230601 [2]安徽建筑大学环境污染控制与废弃物资源化利用安徽省重点实验室,合肥230601 [3]荆州水务集团有限公司,湖北荆州434000

出　　处：《华东师范大学学报（自然科学版）》2024年第1期1-8,112,共9页Journal of East China Normal University(Natural Science)

基　　金：中国科学院科技服务网络计划(KFJ-STS-QYZD-173);安徽省高校自然科学研究项目(KJ2021A0619)。

摘　　要：以合肥市环城河为研究对象,使用线性回归、随机森林、支持向量回归和套索回归等机器学习模型挖掘Landsat8卫星数据和水质参数之间的关系,对遥感影像值的反射率和水质参数进行建模,并比较了4种不同模型的表现.结果显示,随机森林模型的表现最好,对TN、TP、NH3-N反演模型的精度都能达到0.7以上;反演的水质参数浓度分布图表明TN、TP在环城河东北段的污染最严重,而NH3-N则在西南段的污染最严重;从水体富营养化分布图可以看出,环城河东段水体呈现中度营养状态.Taking Huancheng River in Hefei City as the study site,machine learning models such as linear regression,random forest,support vector regression,and lasso regression were utilized to establish the relationship between Landsat8 satellite data and water quality parameters,model the reflectance and water quality parameters of remote sensing image values,and compare the performance of four different models.Results showed that the random forest model performed best,and the accuracy of the inversion models for total nitrogen(TN),total phosphorus(TP),and ammonia nitrogen(NH3-N)was above 0.7.The concentration distribution map of water quality parameters showed that the pollution of TN and TP was the most significant in the northeast section of Huancheng River,while NH3-N was most present in the southwest section.The water eutrophication distribution map shows that the water body in the eastern section of the Huancheng River showed a moderate nutrition state.

关 键 词：机器学习 Landsat8 富营养化评价 

分 类 号：X824[环境科学与工程—环境工程]