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Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir
Iek In Ieong1; Inchio Lou1; Wai Kin Ung2; Kai Meng Mok1
2015-08-03
Source PublicationEnvironmental Modeling and Assessment
ISSN1420-2026
Volume20Issue:4Pages:355-365
Abstract

Principle component regression (PCR), artificial neural network (ANN), and their combination used as data-driven models were selected to apply in this study to predict (based on the current-month variables) and forecast (based on the last 3-month-ahead variables) the phytoplankton dynamics in Macau Main Storage Reservoir (MSR) that is experiencing algal bloom in recent years. The models used the comprehensive 8 years’ monthly water quality data for training and the most recent 3 years’ monthly data for testing. Twenty-four water quality variables including physical, chemical, and biological parameters were involved, and comparisons were made to select the best models that can be applied to MSR. Simulation results revealed that ANN has better accuracy and generalization performance in comparison with PCR both for the prediction and the forecasted model. Using principal component analysis (PCA) for the data, inputs did not show better performance for the ANN, implying that eliminating the uncorrelated variables do not increase the prediction capability for the adopted model. Globally, in contrast with previous studies showing that the hybrid model can handle both linear and nonlinear components of the problems well, the PCR-ANN in this study obtain no better improvement.

KeywordAlgal Bloom Artificial Neural Network Forecast Model Phytoplankton Abundance Prediction Model Principle Component Analysis
DOIhttps://doi.org/10.1007/s10666-014-9433-3
URLView the original
Indexed BySCI
Language英语
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000357286400006
PublisherSPRINGER, VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
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Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorInchio Lou
Affiliation1.Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau, Av. Padre Tomás Pereira Taipa, Macau, SAR, China
2.Laboratory & Research Center, Macao Water Co., Ltd., 718, Avenida do ConselheiroBorja, Macau, SAR, China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Iek In Ieong,Inchio Lou,Wai Kin Ung,et al. Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir[J]. Environmental Modeling and Assessment,2015,20(4):355-365.
APA Iek In Ieong,Inchio Lou,Wai Kin Ung,&Kai Meng Mok.(2015).Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir.Environmental Modeling and Assessment,20(4),355-365.
MLA Iek In Ieong,et al."Using Principle Component Regression, Artificial Neural Network, and Hybrid Models for Predicting Phytoplankton Abundance in Macau Storage Reservoir".Environmental Modeling and Assessment 20.4(2015):355-365.
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