Pollution Control and Risk Assessment for Aquatic Environment

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

  • Yong ZHANG ,
  • Hui WANG ,
  • Chuanhua ZHU ,
  • Hao ZHOU ,
  • Yu ZHAN ,
  • Can LI ,
  • Yifan XIAO ,
  • Lili YANG ,
  • Jiaqi LIU
Expand
  • 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 date: 2023-04-26

  Accepted date: 2023-10-20

  Online published: 2024-01-23

Abstract

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.

Cite this article

Yong ZHANG , Hui WANG , Chuanhua ZHU , Hao ZHOU , Yu ZHAN , Can LI , Yifan XIAO , Lili YANG , Jiaqi LIU . Evaluation of eutrophication by satellite remote sensing based on machine learning: A case study of Huancheng River in Hefei[J]. Journal of East China Normal University(Natural Science), 2024 , 2024(1) : 1 -8, 112 . DOI: 10.3969/j.issn.1000-5641.2024.01.001

References

1 ADUSEI Y Y, QUAYE-BALLARD J, ADJAOTTOR A A, et al.. Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models. The Egyptian Journal of Remote Sensing and Space Sciences, 2021, 24 (3): 825- 833.
2 SHARAF E D E, ZHANG Y, ALAELDIN S.. Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework. International Journal of Remote Sensing, 2017, 38 (4): 1023- 1042.
3 KRISHNARAJ A, HONNASIDDAIAH R.. Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin. Environmental Science and Pollution Research, 2022, 27 (29): 64939- 64958.
4 SAGAN V, PETERSON K T, MAIMAITIJIANG M, et al.. Monitoring inland water quality using remote sensing: Potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews, 2020, 205, 103187.
5 CHEN J, FU R, CHEN S, et al. Remote sensing big data for water environment monitoring: Current status, challenges, and future prospects [J]. Earth’s Future, 2022, 10(2): e2021EF002289.
6 JIANG Q O, XU L D, WANG M L, et al.. Retrieval model for total nitrogen concentration based on UAV hyper spectral remote sensing data and machine learning algorithms – A case study in the Miyun Reservoir, China. Ecological Indicators, 2021, 124, 107356.
7 WU C F, WU J P, Qi J G, et al.. Empirical estimation of total phosphorus concentration in the mainstream of the Qiantang River in China using Landsat TM data. International Journal of Remote Sensing, 2010, 21 (9): 2309- 2324.
8 ZHAO Y B, YU T, HU B L, et al.. Retrieval of water quality parameters based on near-surface remote sensing and machine learning algorithm. Remote Sensing, 2022, 14 (21): 5305.
9 王延军, 徐敏, 孟凡生, 等.. 长江中游黄盖湖富营养化趋势分析及原因诊断. 湖泊科学, 2023, 35 (4): 1- 14.
10 HE Y, GONG Z, ZHENG Y, et al.. Inland reservoir water quality inversion and eutrophication evaluation using BP neural network and remote sensing imagery: A case study of Dashahe Reservoir. Water, 2021, 13 (20): 2844.
11 生态环境部. 水质 总氮的测定 碱性过硫酸钾消解紫外分光光度法: HJ 636-2012 [S]. 北京: 中国环境科学出版社, 2012.
12 生态环境部. 水质 氨氮的测定 纳氏试剂分光光度法: HJ 535-2009 [S]. 北京: 中国环境科学出版社, 2009.
13 生态环境部. 水质 总磷的测定 钼酸铵分光光度法: GB11893-89 [S]. 北京: 中国标准出版社, 1989.
14 GUO R, DENG R R, LI J Y, et al.. Remote sensing retrieval of total nitrogen in the Pearl River Delta based on Landsat8. Water, 2022, 14 (22): 3710.
15 PANDIS N.. Correlation and linear regression. American Journal of Orthodontics and Dentofacial Orthopedics, 2016, 149 (2): 299.
16 TIAN S, GUO H, XU W, et al.. Remote sensing retrieval of inland water quality parameters using Sentinel-2 and multiple machine learning algorithms. Environmental Science and Pollution Research, 2022, 30, 18617- 18630.
17 XIAO X, ZHANG T, ZHONG X, et al.. Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data. Remote Sensing of Environment, 2018, 210, 48- 64.
18 NAJAFZADEH M, NIAZMARDI S.. A novel multiple-kernel support vector regression aigorithm for estimation of water quality parameters. Natural Resources Research, 2021, 30, 3761- 3775.
19 SHEN H, MA Y, LIN C, et al.. Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction. Reliability Engineering and System Safety, 2021, 206, 107268.
20 LEE J H, SHI Z, GAO Z.. On LASSO for predictive regression. Journal of Econometrics, 2021, 229 (2): 322- 349.
21 魏广芬, 李梦递, 赵捷.. 基于 DFI-RSE 电子鼻传感器阵列优化的葡萄酒 SO2 检测. 农业工程学报, 2022, 38 (7): 291- 299.
22 GHOSH S M, BEHERA M D.. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography, 2018, 96, 29- 40.
23 殷飞. 水体营养状况评价方法研究 [J]. 科技创新与应用, 2017, 30: 99-100.
24 陈昭明, 王伟, 赵迎.. 三峡水库支流水体富营养化现状及防治策略. 环境工程, 2019, 37 (4): 32- 37.
Outlines

/