Deep-Learning-Based Approach for Prediction of Algal Blooms

Feng Zhang, Yuanyuan Wang, Minjie Cao, Xiaoxiao Sun, Zhenhong Du, Renyi Liu and Xinyue Ye
Additional contact information
Feng Zhang: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Yuanyuan Wang: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Minjie Cao: Second Institute of Oceanography, 36 N. Baochu Road, Hangzhou 310012, China
Xiaoxiao Sun: School of Earth Sciences, Zhejiang University, 38 Zheda Road, Hangzhou 310027, China
Zhenhong Du: Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China
Renyi Liu: Zhejiang Provincial Key Laboratory of Geographic Information Science, 148 Tianmushan Road, Hangzhou 310028, China
Xinyue Ye: Department of Geography, Kent State University, Kent, OH 44240, USA

Sustainability, 2016, vol. 8, issue 10, 1-12

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.

Keywords: algal blooms prediction; deep learning; deep belief networks; East China; coastal areas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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