中国科学院重庆绿色智能技术研究院机构知识库

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	Employing hybrid deep learning for near-real-time forecasts of sensor-based algal parameters in a Microcystis bloom-dominated lake
	Wang, Lan1,2,6; Shan, Kun2; Yi, Yang2; Yang, Hong3; Zhang, Yanyan4; Xie, Mingjiang2; Zhou, Qichao5; Shang, Mingsheng2
	2024-04-20
摘要	Harmful cyanobacterial blooms (CyanoHABs) are increasingly impacting the ecosystem of lakes, reservoirs and estuaries globally. The integration of real-time monitoring and deep learning technology has opened up new horizons for early warnings of CyanoHABs. However, unlike traditional methods such as pigment quantification or microscopy counting, the high-frequency data from in-situ fluorometric sensors display unpredictable fluctuations and variability, posing a challenge for predictive models to discern underlying trends within the timeseries sequence. This study introduces a hybrid framework for near-real-time CyanoHABs predictions in a cyanobacterium Microcystis-dominated lake - Lake Dianchi, China. The proposed model was validated using hourly Chlorophyll-a (Chl a) concentrations and algal cell densities. Our results demonstrate that applying decomposition-based singular spectrum analysis (SSA) significantly enhances the prediction accuracy of subsequent CyanoHABs models, particularly in the case of temporal convolutional network (TCN). Comparative experiments revealed that the SSA-TCN model outperforms other SSA-based deep learning models for predicting Chl a (R2 = 0.45-0.93, RMSE = 2.29-5.89 mu g/L) and algal cell density (R2 = 0.63-0.89, RMSE = 9489.39-16,015.37 cells/mL) at one to four steps ahead predictions. The forecast of bloom intensities achieved a remarkable accuracy of 98.56 % and an average precision rate of 94.04 % +/- 0.05 %. In addition, scenarios involving various input combinations of environmental factors demonstrated that water temperature emerged as the most effective driver for CyanoHABs predictions, with a mean RMSE of 2.94 +/- 0.12 mu g/L, MAE of 1.55 +/- 0.09 mu g/L, and R2 of 0.83 +/- 0.01. Overall, the newly developed approach underscores the potential of a welldesigned hybrid deep-learning framework for accurately predicting sensor-based algal parameters. It offers novel perspectives for managing CyanoHABs through online monitoring and artificial intelligence in aquatic ecosystems.
关键词	Cyanobacterial blooms Real-time monitoring Early warning Deep -learning modelling Time series decomposition Interpretable framework
DOI	10.1016/j.scitotenv.2024.171009
发表期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
卷号	922页码:14
通讯作者	Shan, Kun(shankun@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:001198969800001
语种	英语