Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model
Muyuan Liu, Jing Hu, Yuzhou Huang, Junyu He, Kokoette Effiong, Tao Tang, Shitao Huang, Yuvna Devi Perianen, Feier Wang, Ming Li, Xi Xiao
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.