Deep-learning-based approach for prediction of algal blooms

Feng Zhang, Yuanyuan Wang, Minjie Cao, Xiaoxiao Sun, Zhenhong Du, Renyi Liu, Xinyue Ye

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented-a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

Original languageEnglish (US)
Article number1060
JournalSustainability (Switzerland)
Issue number10
StatePublished - Oct 21 2016
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Environmental Science (miscellaneous)
  • Geography, Planning and Development
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Management, Monitoring, Policy and Law
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment


  • Algal blooms prediction
  • Coastal areas
  • Deep belief networks
  • Deep learning
  • East China


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