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
Sustainability 2016, 8(10), 1060; https://doi.org/10.3390/su8101060
Received: 25 July 2016
/
Revised: 26 September 2016
/
Accepted: 14 October 2016
/
Published: 21 October 2016
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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
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. https://doi.org/10.3390/su8101060
AMA Style
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(10):1060. https://doi.org/10.3390/su8101060
Chicago/Turabian StyleZhang, Feng, Yuanyuan Wang, Minjie Cao, Xiaoxiao Sun, Zhenhong Du, Renyi Liu, and Xinyue Ye. 2016. "Deep-Learning-Based Approach for Prediction of Algal Blooms" Sustainability 8, no. 10: 1060. https://doi.org/10.3390/su8101060
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
