UM
Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater
Inchio Lou1; Zhengchao Xie1; Wai Kin Ung2; Kai Meng Mok1
2015-10-01
Source PublicationApplied Mathematical Modelling
ISSN0307-904X
Volume39Issue:19Pages:5907-5916
Abstract

Algae-releasing cyanotoxins are cancer-causing and very harmful to the human being. Therefore, it is of great significance to model how the algae population dynamically changes in freshwater reservoirs. But the practical modeling is very difficult because water variables and their internal mechanism are very complicated and non-linear. So, in order to alleviate the algal bloom problems in Macau Main Storage Reservoir (MSR), this work proposes and develops a hybrid intelligent model combining Support Vector Regression (SVR) and Particle Swarm Optimization (PSO) to yield optimal control of parameters that predict and forecast the phytoplankton dynamics. In this process, collected data for current month's variables and previous months' variables are used for model predict and forecast, respectively. In the correlation analysis of 23 water variables that monitored monthly, 15 variables such as alkalinity, Bicarbonate (HCO3-), dissolved oxygen (DO), total nitrogen (TN), turbidity, conductivity, nitrate, suspended solid (SS) and total organic carbon (TOC) are selected, and data from 2001 to 2008 for each of these selected variables are used for training, while data from 2009 to 2011 which are the most recent three years are used for testing. It can be seen from the numerical results that the prediction and forecast powers are respectively estimated at approximately 0.767 and 0.876, and naturally it can be concluded that the newly proposed PSO-SVR is working well and can be adopted for further studies.

KeywordAlgal Bloom Particle Swarm Optimization Phytoplankton Abundance Prediction And Forecast Models Support Vector Regression
DOIhttps://doi.org/10.1016/j.apm.2015.04.001
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEngineering ; Mathematics ; Mechanics
WOS SubjectEngineering, Multidisciplinary ; Mathematics, Interdisciplinary Applications ; Mechanics
WOS IDWOS:000362609000016
PublisherELSEVIER SCIENCE INC, 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA
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Cited Times [WOS]:12   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorZhengchao Xie
Affiliation1.Faculty of Science and Technology, University of Macau, Macau
2.Laboratory & Research Center, Macao Water Co. Ltd., Macau
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Inchio Lou,Zhengchao Xie,Wai Kin Ung,et al. Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater[J]. Applied Mathematical Modelling,2015,39(19):5907-5916.
APA Inchio Lou,Zhengchao Xie,Wai Kin Ung,&Kai Meng Mok.(2015).Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater.Applied Mathematical Modelling,39(19),5907-5916.
MLA Inchio Lou,et al."Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater".Applied Mathematical Modelling 39.19(2015):5907-5916.
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