Journal of East China Normal University(Natural Science) ›› 2024, Vol. 2024 ›› Issue (1): 1-8, 112.doi: 10.3969/j.issn.1000-5641.2024.01.001

• Pollution Control and Risk Assessment for Aquatic Environment •     Next Articles

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

Yong ZHANG1,2(), Hui WANG1,3, Chuanhua ZHU1, Hao ZHOU1, Yu ZHAN1, Can LI1, Yifan XIAO1, Lili YANG1, Jiaqi LIU1   

  1. 1. School of Environmental and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China
    2. Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230601, China
    3. Jingzhou Water Group Co. Ltd., Jingzhou, Hubei 434000, China
  • Received:2023-04-26 Accepted:2023-10-20 Online:2024-01-25 Published:2024-01-23


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.

Key words: machine learning, Landsat8, eutrophication evaluation

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