Figure 7
(a) Logistic regression model for classifying cyanobacteria and eukaryotic algal; (b) Bayesian hierarchical linear model for estimating PCB based on APCBI.
Contexts in source publication
Context 1
... available matchups (N=28) of in situ sampling data of phytoplankton species composition involving algal abundance and biomasses of all eukaryote and cyanobacteria species from June to November (covering cyanobacteria growing season) in 2016 was used to investigate the relationship between the aggregate index and these algal species composition parameters. The aggregate index was found to correlated well with proportion of cyanobacterial cell counts (Figure 7a), and the proportion of cyanobacterial biomass in total phytoplankton biomass (Figure 7b), respectively. Logistic regression model was applied to obtain the decision boundary for cyanobacteria and eukaryotic algal according to the reference proposed by Zhou et al.2019, that the relative cell counting of the single bloom-dominated groups exceeded 60% [17]. ...
Context 2
... available matchups (N=28) of in situ sampling data of phytoplankton species composition involving algal abundance and biomasses of all eukaryote and cyanobacteria species from June to November (covering cyanobacteria growing season) in 2016 was used to investigate the relationship between the aggregate index and these algal species composition parameters. The aggregate index was found to correlated well with proportion of cyanobacterial cell counts (Figure 7a), and the proportion of cyanobacterial biomass in total phytoplankton biomass (Figure 7b), respectively. Logistic regression model was applied to obtain the decision boundary for cyanobacteria and eukaryotic algal according to the reference proposed by Zhou et al.2019, that the relative cell counting of the single bloom-dominated groups exceeded 60% [17]. ...
Context 3
... hierarchical (Multilevel) linear model was constructed to model the relationship between the aggregate cyanobacterial biomass proportion index (ACBPI) and PCB owing to its constructing hyperpriors on group-level parameter to allow the model sharing the individual properties of PCB among the groups. As shown in Figure 7b, matchups of in situ PCB were divided based on three regions. The coefficient of determination (R 2 ) for the regression model is 0.73 with root mean square error (RMSE) of 13.58%. ...
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