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Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China

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Abstract

Algal blooms caused by climate change and human activities have received considerable attention in recent years. Since chlorophyll a (Chl-a) can be used as an indicator of phytoplankton biomass, it has been selected as a direct indicator for monitoring and early warning of algal blooms. With the development of artificial intelligence, data-driven approaches with small sample data and high accuracy prediction have been gradually applied to water quality prediction. This study aimed at using environment factors (water quality and meteorological data) to assist the prediction of Chl-a concentration based on the optimization support vector machine (SVM) model. The most relevant environment factors were extracted from the commonly used environment factors according to the method of cosine similarity. The traditional particle swarm optimization (PSO) algorithm was adopted to optimize the ANN and SVM models, respectively. Then, the better prediction model PSO-SVM can be obtained according to the results of three scientific evaluation indicators. The latest optimization algorithm of grey wolf optimizer (GWO) was also proposed to optimize the SVM to realize high-accuracy Chl-a concentration predication. The GWO-SVM model achieved higher accuracy than the other models both in training and validation processes. Therefore, the dimension of the input vector could be reduced with using the cosine similarity method, and the prediction of Chl-a concentration in high accuracy and the early warning of algal blooms in the study area of this paper could also achieved.

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

The code used during the current study is available from the corresponding author on reasonable request.

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Acknowledgements

The supports of this work is by the Natural Science Foundation of Hubei Province (2020CFB292). And the authors wish to thank Chongqing Academy of Environmental Science for providing the water quality dataset used in this research. Additionally, we greatly appreciate the helpful comments on the manuscript by the anonymous reviewers.

Funding

The support of this work is by the Natural Science Foundation of Hubei Province (2020CFB292).

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Authors and Affiliations

  1. School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Jingjing Xia

  2. Institute of Environmental Industry of Huangshi, Hubei Polytechnic University, Huangshi, 435003, China

    Jingjing Xia

  3. School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, China

    Jingjing Xia

  4. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Jin Zeng

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  1. Jingjing Xia
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Dr. X is mainly responsible for data processing and model establishment and Dr. Z mainly responsible for result analysis and paper writing.

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Correspondence to Jin Zeng.

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No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously and not under consideration for publication elsewhere.

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Xia, J., Zeng, J. Early warning of algal blooms based on the optimization support vector machine regression in a typical tributary bay of the Three Gorges Reservoir, China. Environ Geochem Health 44, 4719–4733 (2022). https://doi.org/10.1007/s10653-022-01203-1

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