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Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs
Lou, Inchio1; Xie, Zhengchao1; Ung, Wai Kin2; Mok, Kai Meng1
2016-01
Source PublicationNeural Computing and Applications
ISSN0941-0643
Volume27Issue:1Pages:19-26
Abstract

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult in modeling its growth. Recently, extreme learning machine (ELM) was reported to have advantages of only requirement of a small amount of samples, high degree of prediction accuracy and long prediction period to solve the nonlinear problems. In this study, the ELM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir are proposed, in which the water parameters of pH, SiO2, and some other water variables selected from the correlation analysis were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast (based on data on the previous 1st, 2nd, 3rd and 12th months) powers were estimated as approximately 0.83 and 0.90, respectively, showing that the ELM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir. © 2014, Springer-Verlag London.

KeywordAlgal Bloom Phytoplankton Abundance Extreme Leaning Machine Prediction And Forecast Models
DOIhttps://doi.org/10.1007/s00521-013-1538-0
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000369995700004
PublisherSPRINGER, 233 SPRING ST, NEW YORK, NY 10013 USA
The Source to ArticleEngineering Village
Fulltext Access
Citation statistics
Cited Times [WOS]:6   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorXie, Zhengchao
Affiliation1.Faculty of Science and Technology, University of Macau, Taipa, Macau SAR
2.Laboratory and Research Center, Macao Water Co. Ltd, Taipa, Macau SAR
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Lou, Inchio,Xie, Zhengchao,Ung, Wai Kin,et al. Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs[J]. Neural Computing and Applications,2016,27(1):19-26.
APA Lou, Inchio,Xie, Zhengchao,Ung, Wai Kin,&Mok, Kai Meng.(2016).Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs.Neural Computing and Applications,27(1),19-26.
MLA Lou, Inchio,et al."Freshwater algal bloom prediction by extreme learning machine in Macau Storage Reservoirs".Neural Computing and Applications 27.1(2016):19-26.
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