title: A novel single-parameter approach for forecasting algal blooms.
abstract: Harmful algal blooms frequently occur globally, and forecasting could constitute an essential proactive strategy for bloom control. To decrease the cost of aquatic environmental monitoring and increase the accuracy of bloom forecasting, a novel single-parameter approach combining wavelet analysis with artificial neural networks (WNN) was developed and verified based on daily online monitoring datasets of algal density in the Siling Reservoir, China and Lake Winnebago, U.S.A. Firstly, a detailed modeling process was illustrated using the forecasting of cyanobacterial cell density in the Chinese reservoir as an example. Three WNN models occupying various prediction time intervals were optimized through model training using an early stopped training approach. All models performed well in fitting historical data and predicting the dynamics of cyanobacterial cell density, with the best model predicting cyanobacteria density one-day ahead (r = 0.986 and mean absolute error = 0.103 × 104cells mL-1). Secondly, the potential of this novel approach was further confirmed by the precise predictions of algal biomass dynamics measured as chl a in both study sites, demonstrating its high performance in forecasting algal blooms, including cyanobacteria as well as other blooming species. Thirdly, the WNN model was compared to current algal forecasting methods (i.e. artificial neural networks, autoregressive integrated moving average model), and was found to be more accurate. In addition, the application of this novel single-parameter approach is cost effective as it requires only a buoy-mounted fluorescent probe, which is merely a fraction (∼15%) of the cost of a typical auto-monitoring system. As such, the newly developed approach presents a promising and cost-effective tool for the future prediction and management of harmful algal blooms.
authors.affiliation: Ocean College, Zhejiang University, Zhoushan, PR China; College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, PR China; Key Laboratory for Water Pollution Control and Environmental Safety, Zhejiang Province, PR China.
authors.forename: Xi
authors.initials: X
authors.lastname: Xiao
authors.affiliation: Ocean College, Zhejiang University, Zhoushan, PR China; College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, PR China; Key Laboratory for Water Pollution Control and Environmental Safety, Zhejiang Province, PR China.
authors.forename: Junyu
authors.initials: J
authors.lastname: He
authors.affiliation: College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, PR China; Key Laboratory for Water Pollution Control and Environmental Safety, Zhejiang Province, PR China; School of Environment and Energy, South China University of Technology, Guangzhou, PR China.
authors.forename: Haomin
authors.initials: H
authors.lastname: Huang
authors.affiliation: Joseph J. Zilber School of Public Health, University of Wisconsin - Milwaukee, USA.
authors.forename: Todd R
authors.initials: TR
authors.lastname: Miller
authors.affiliation: Ocean College, Zhejiang University, Zhoushan, PR China.
authors.forename: George
authors.initials: G
authors.lastname: Christakos
authors.affiliation: Aquatic Ecology and Ecosystem Studies, School of Civil, Environmental and Mining Engineering, The University of Western Australia, Crawley, WA, Australia.
authors.forename: Elke S
authors.initials: ES
authors.lastname: Reichwaldt
authors.affiliation: Aquatic Ecology and Ecosystem Studies, School of Civil, Environmental and Mining Engineering, The University of Western Australia, Crawley, WA, Australia.
authors.forename: Anas
authors.initials: A
authors.lastname: Ghadouani
authors.affiliation: Department of Integrative Biology, Michigan State University, East Lansing, MI, USA.
authors.forename: Shengpan
authors.initials: S
authors.lastname: Lin
authors.affiliation: College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, PR China.
authors.forename: Xinhua
authors.initials: X
authors.lastname: Xu
authors.affiliation: College of Environmental & Resource Sciences, Zhejiang University, Hangzhou, PR China; Key Laboratory for Water Pollution Control and Environmental Safety, Zhejiang Province, PR China. Electronic address: shijiyan@zju.edu.cn.
authors.forename: Jiyan
authors.initials: J
authors.lastname: Shi
mesh_terms: D000458:Cyanobacteria; D004784:Environmental Monitoring; D005068:Eutrophication; D005544:Forecasting; D057097:Harmful Algal Bloom; D060106:Lakes / Q000382:microbiology
keywords: Artificial neural network; Forecasting; Harmful algal bloom; Phytoplankton; Single-parameter; Wavelet analysis