Probabilistic prediction of algal blooms from basic water quality parameters by Bayesian scale-mixture of skew-normal model
Bibliographic Data
| ID | 15545802 |
|---|---|
| Authors | Muyuan Liu (Zhejiang Ocean University), Jing Hu (0009-0007-8446-0862, Zhejiang University), Yuzhou Huang (0000-0002-2818-867X, Zhejiang University), Junyu He (0000-0003-1873-3125, Zhejiang Ocean University), Kokoette Effiong (0000-0002-5994-2022, Zhejiang Ocean University), Tao Tang (0009-0007-6735-8479, Zhejiang University), Shitao Huang (Zhejiang Ocean University), Yuvna Devi Perianen (0000-0002-0515-4223, Zhejiang University), Feier Wang (0000-0003-0411-3494, Zhejiang University), Ming Li (0000-0001-7109-5084, East China Normal University), Xi Xiao (0009-0000-0931-6982, Zhoushan Hospital, corresponding author) |
| Year | 2022 |
| Volume | 18 |
| Issue | 1 |
| Pages | 014034-014034 |
| Publication date | 2022-12-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/acaf11 |
| OpenAlex | W4313332385 |
| Language | EN |
| References cited | 66 |
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
Algal bloom · Bayesian network · Bayesian probability · Biology · Biomass (ecology · Machine learning · Nutrient · Phytoplankton · Probabilistic logic · Scale (ratio · Skew · Statistics · Water quality · Computer Science · Environmental Science · Fish Ecology and Management Studies · Hydrological Forecasting Using AI · Mathematics · Water Quality Monitoring Technologies · Artificial Intelligence · Ecology
| Citation velocity | historical |
|---|---|
| Highly cited | No |