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- W4313332385 abstract "Abstract The timeliness of monitoring is essential to algal bloom management. However, acquiring algal bio-indicators can be time-consuming and laborious, and bloom biomass data often contain a large proportion of extreme values limiting the predictive models. Therefore, to predict algal blooms from readily water quality parameters (i.e. dissolved oxygen, pH, etc), and to provide a novel solution to the modeling challenges raised by the extremely distributed biomass data, a Bayesian scale-mixture of skew-normal (SMSN) model was proposed. In this study, our SMSN model accurately predicted over-dispersed biomass variations with skewed distributions in both rivers and lakes (in-sample and out-of-sample prediction R 2 ranged from 0.533 to 0.706 and 0.412 to 0.742, respectively). Moreover, we successfully achieve a probabilistic assessment of algal blooms with the Bayesian framework (accuracy >0.77 and macro- F 1 score >0.72), which robustly decreased the classic point-prediction-based inaccuracy by up to 34%. This work presented a promising Bayesian SMSN modeling technique, allowing for real-time prediction of algal biomass variations and in-situ probabilistic assessment of algal bloom." @default.
- W4313332385 created "2023-01-06" @default.
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- W4313332385 date "2023-01-01" @default.
- W4313332385 modified "2023-09-26" @default.
- W4313332385 title "Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model" @default.
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- W4313332385 doi "https://doi.org/10.1088/1748-9326/acaf11" @default.
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