Deep-learning-based approach for prediction of algal blooms

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Abstract

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)
Volume8
Issue number10
DOIs
StatePublished - Oct 21 2016
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Management, Monitoring, Policy and Law
  • Renewable Energy, Sustainability and the Environment

Keywords

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

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