Early warning of cyanobacterial blooms based on polarized light scattering powered by machine learning. (November 2021)

• Record Type: Journal Article
• Authors: Wang, Hongjian; Li, Jiajin; Liao, Ran; Tao, Yi; Peng, Liang; Li, Hening; Deng, Hanbo; Ma, Hui
• Is Part Of: Measurement. Volume 184(2021)
• Publication Date: 2021-11
• Subjects: Cyanobacterial blooms -- Gas vesicles -- Early warning -- Polarized light scattering -- Machine learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109902
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Characterization of feature states of cyanobacterial cells on their vertical movements. Polarization parameters indicate the stages of the cyanobacterial blooms. Machine learning algorithms help extract information from polarization parameters. A potential in-situ early warning strategy for the cyanobacterial blooms. Abstract: Cyanobacterial blooms have become an urgent threat to the aquatic ecosystem, but early warning of the blooms is still challenging for the research community. In this paper, a method based on polarized light scattering and powered by machine learning is proposed to in-situ early warn the cyanobacterial blooms. In this work, the wild types of Microcystis are treated and the cells are individually measured to obtain their polarization parameters. The experimental results show that machine learning algorithms can be used to well identify the states of the Microcystis cells, and the compositions of the mixed samples can be effectively retrieved by this method. Subsequently, one application strategy is suggested to early warn the blooms, which is potential and powerful to achieve the in-situ early warning of cyanobacterial blooms in the future.