A deep learning method for cyanobacterial harmful algae blooms prediction in Taihu Lake, China

• Published in: Harmful algae 2022-03, Vol.113, p.102189-102189, Article 102189
• Main Authors: Cao, Hongye, Han, Ling, Li, Liangzhi
• Format: Article
• Language: English
• Subjects: CNN; CyanoHABs; LSTM; MODIS; Taihu Lake
• ISSN: 1568-9883; 1878-1470
• DOI: 10.1016/j.hal.2022.102189

•In terms of time coverage distribution, there were peaks of CyanoHABs in 2007 and 2017.•The meteorological factors closely related to the CyanoHABs outbreak in Taihu Lake are mainly temperature, relative humidity, wind speed, and precipitation;.•The CNN-LSTM integrated model has better prediction of the CyanoHABs area. Cyanobacterial Harmful Algae Blooms (CyanoHABs) in the eutrophic lakes have become a global environmental and ecological problem. In this study, a CNN-LSTM integrated model for predicting the CyanoHABs area was proposed and applied to the prediction of the CyanoHABs area in Taihu Lake. Firstly, the time-series data of the CyanoHABs area in Taihu Lake for 20 years were accurately obtained using MODIS images from 2000 to 2019 based on the FAI method. Then, a principal component analysis was performed on the daily meteorological data for the month before the outbreak of CyanoHABs in Taihu Lake from 2000 to 2019 to determine the meteorological factors closely related to the outbreak of CyanoHABs. Finally, the features of CyanoHABs area and meteorological data were extracted by Convolutional Neural Networks (CNN) model and used as the input of Long Short Term Memory Network (LSTM). An integrated CNN-LSTM model approach was constructed for predicting the CyanoHABs area. The results show that high R2 (0.91) and low mean relative error (17.42%) verified the validity of the FAI index to extract the CyanoHABs area in Taihu Lake; the meteorological factors closely related to the CyanoHABs outbreak in Taihu Lake are mainly temperature, relative humidity, wind speed, and precipitation; the CNN-LSTM integrated model has better prediction effect for both training and test sets compared with the CNN and LSTM models. This study provides an effective method for predicting temporal changes in the CyanoHABs area and offers new ideas for scientific and effective regulation of inland water safety.