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Cited by 28Aquatic Ecosystems and Phytoplankton DynamicsWater Quality Monitoring TechnologiesHydrological Forecasting Using AI

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A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms

Karimian, Hamed, Huang, Jinhuang, Chen, Youliang, Wang, Zhaoru, Huang, Jinsong·2023·Environmental Science and Pollution Research
DOI 10.1007/s11356-023-27886-2
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Abstract

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2 m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

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Metadata

Authors
Karimian, H., Huang, J., Chen, Y., Wang, Z., & Huang, J.
Affiliations
  • Jiangsu Ocean University (CN)
  • Jiangxi University of Science and Technology (CN)
Corresponding author
Chen, Youliang
ORCID
Venue
Environmental Science and Pollution Research
Publisher
Springer Science and Business Media LLC
Published
2023-06-07
Volume
30
Issue
32
Pages
79402-79422
ISSN
  • 0944-1344 (ISSN-L)
  • 1614-7499
DOI
10.1007/s11356-023-27886-2
OpenAlex
W4379745574
Cited by
28
References
0
Citations by year
2026: 10 · 2025: 5 · 2024: 12 · 2023: 1
FWCI
2.33
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Environmental Science and Pollution Research (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
  • Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
  • Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
Keywords
eutrophication, algal blooms, water quality monitoring, GF-1 satellite imagery, Sentinel-2, multi-source data fusion, high spatial resolution
MeSH terms
Machine Learning, Chlorophyll A, Big Data, Algorithms, Chlorophyll, Environmental Monitoring, Eutrophication, Ecosystem, Lakes

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Karimian, H., Huang, J., Chen, Y., Wang, Z., & Huang, J. (2023). A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-023-27886-2