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Title: Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model
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 predictionR2ranged 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-F1score >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 andin-situprobabilistic assessment of algal bloom.

 
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Award ID(s):
2025982
NSF-PAR ID:
10492674
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Environmental Research Letters
Date Published:
Journal Name:
Environmental Research Letters
Volume:
18
Issue:
1
ISSN:
1748-9326
Page Range / eLocation ID:
014034
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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