UM  > 科技學院  > 土木及環境工程系
Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs
Wang, Yanjie1; Xie, Zhengchao2; Lou, InChio1; Ung, Wai Kin3; Mok, Kai Meng1
2017
Source PublicationEngineering Computations
ISSN0264-4401
Volume34Issue:2Pages:664-679
Abstract

Purpose – The purpose of this paper is to examine the applicability and capability of models based on a genetic algorithm and support vector machine (GA-SVM) and a genetic algorithm and relevance vector machine (GA-RVM) for the prediction of phytoplankton abundances associated with algal blooms in a Macau freshwater reservoir, and compare their performances with an artificial neural network (ANN) model.

Design/methodology/approach – The hybrid models GA-SVM and GA-RVM were developed for the optimal control of parameters for predicting (based on the current month’s variables) and forecasting (based on the previous three months’ variables) phytoplankton dynamics in a Macau freshwater reservoir, MSR, which has experienced cyanobacterial blooms in recent years. There were 15 environmental parameters, including pH, SiO2, alkalinity, bicarbonate (HCO3), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate (NO3), orthophosphate (PO4 3), total phosphorus (TP), suspended solids (SS) and total organic carbon (TOC) selected from the correlation analysis, with eight years (2001-2008) of data for training, and the most recent three years (2009-2011) for testing.

Findings – For both accuracy performance and generalized performance, the ANN, GA-SVM and GA-RVM had similar predictive powers of R2 of 0.73-0.75. However, whereas ANN and GA-RVM models showed very similar forecast performances, GA-SVM models had better forecast performances of R2 (0.862), RMSE (0.266) and MAE (0.0710) with the respective parameters of 0.987, 0.161 and 0.032 optimized using GA.

KeywordAlgal Bloom Ga-rvm Ga-svm Phytoplankton Abundance Prediction And Forecast Models
DOIhttps://doi.org/10.1108/EC-11-2015-0356
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering ; Mathematics ; Mechanics
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Mechanics
WOS IDWOS:000404766700021
PublisherEMERALD GROUP PUBLISHING LTD
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Affiliation1.Faculty of Science and Technology, The University of Macau, Macau, China
2.School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
3.Macao Water Co. Ltd., Macau, China
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Wang, Yanjie,Xie, Zhengchao,Lou, InChio,et al. Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs[J]. Engineering Computations,2017,34(2):664-679.
APA Wang, Yanjie,Xie, Zhengchao,Lou, InChio,Ung, Wai Kin,&Mok, Kai Meng.(2017).Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs.Engineering Computations,34(2),664-679.
MLA Wang, Yanjie,et al."Algal bloom prediction by support vector machine and relevance vector machine with genetic algorithm optimization in freshwater reservoirs".Engineering Computations 34.2(2017):664-679.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Yanjie]'s Articles
[Xie, Zhengchao]'s Articles
[Lou, InChio]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Yanjie]'s Articles
[Xie, Zhengchao]'s Articles
[Lou, InChio]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Yanjie]'s Articles
[Xie, Zhengchao]'s Articles
[Lou, InChio]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.