Improved predictive performance of cyanobacterial blooms using a hybrid statistical and deep-learning method

Improved predictive performance of cyanobacterial blooms using a hybrid statistical and deep-learning method

Cyanobacterial harmful algal blooms (CyanoHABs) threaten ecosystem functioning and human health at both regional and global levels, and this threat is likely to become more frequent and severe under climate change. Predictive information can help local water managers to alleviate or manage the adver...

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Bibliographic Details
Main Authors: Hu Li, Chengxin Qin, Weiqi He, Fu Sun, Pengfei Du
Format: Article
Language:English
Published: IOP Publishing 2021-01-01
Series:Environmental Research Letters
Subjects:
cyanobacterial blooms
time-series prediction
ARIMA
LSTM
hybrid model
predictive performance
Online Access:https://doi.org/10.1088/1748-9326/ac302d

Internet

https://doi.org/10.1088/1748-9326/ac302d

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