UM
A prediction model for phytoplankton abundance based on relevance vector machine
Zhengchao Xie1; Inchio Lou2; Wai Kin Ung3; Kai Meng Mok2
2012-01-02
Conference Namethe First International Conference on Engineering and Technology Innovation 2011 (ICETI2011)
Source PublicationApplied Mechanics and Materials
Volume145
Pages314-319
Conference DateNovember 11-15, 2011
Conference PlaceKenting, Taiwan
Abstract

Freshwater algal bloom is caused by rapid increases or accumulations of phytoplankton abundance due to the excess of nutrients in eutrophic lakes or reservoirs. The population dynamics in such ecosystem is difficult to explain and predict due to the high non-linearity of the relationship between phytoplankton abundance and water variables. Thus the capacity of model is a crucial point for system simulation and information abstraction about the target ecosystem. Recently relevance vector machine (RVM) has been reported to be able to work more effectively with simpler algorithm, faster convergence and better accuracy than other prediction approaches, such as artificial neural network (ANN). This work for the first time adopts the RVM to develop a prediction model for phytoplankton abundance given ten water parameters including temperature, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate (PO4 ), total phosphorus (TP), TN/TP, hydraulic retention time (HRT) and water level in Macau Reservoir. The measured data are used to test and validate the model for predicting the phytoplankton abundance. The preliminary results show that the RVM based model is feasible in understanding the algal bloom problem and simulating the onset of algal bloom caused by phytoplankton abundance, thus providing a useful guide for practical algal bloom control.

KeywordPhytoplankton Abundance Prediction Model Relevance Vector Machine Reservoir
DOIhttps://doi.org/10.4028/www.scientific.net/AMM.145.314
URLView the original
Language英语
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Full Department of Electromechanical Engineering, University of Macau, Macau SAR, China
2.Department of Civil and Environmental Engineering, University of Macau, Macau SAR, China
3.Water Quality Research Division, Macau Water Supply Co.Ltd, University of Macau, Macau SAR, China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhengchao Xie,Inchio Lou,Wai Kin Ung,et al. A prediction model for phytoplankton abundance based on relevance vector machine[C],2012:314-319.
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