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Sustainability 2016, 8(10), 1060; doi:10.3390/su8101060

Deep-Learning-Based Approach for Prediction of Algal Blooms

1
School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
2
Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China
3
Second Institute of Oceanography, 36 N. Baochu Road, Hangzhou 310012, China
4
Department of Geography, Kent State University, Kent, OH 44240, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Marc Rosen
Received: 25 July 2016 / Revised: 26 September 2016 / Accepted: 14 October 2016 / Published: 21 October 2016
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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. View Full-Text
Keywords: algal blooms prediction; deep learning; deep belief networks; East China; coastal areas algal blooms prediction; deep learning; deep belief networks; East China; coastal areas
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Zhang, F.; Wang, Y.; Cao, M.; Sun, X.; Du, Z.; Liu, R.; Ye, X. Deep-Learning-Based Approach for Prediction of Algal Blooms. Sustainability 2016, 8, 1060.

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