NDLI: A comparative study of statistical learning methods to predict eutriphication tendency in a reservoir, northeast China

Content Provider	IEEE Xplore Digital Library
Author	Jiping Jiang Peng Wang Zaixing Tian Liang Guo Yi Wang
Copyright Year	2011
Description	Author affiliation: School of Municipal and Environmental Engineering, Harbin Institute of Technology, China (Jiping Jiang; Peng Wang; Zaixing Tian; Liang Guo; Yi Wang)
Abstract	In order to early warn the alga bloom in a reservoir (drinking water source) in northeast China, we carried out a comparative investigation on three statistical machine learning methods to construct suitable one-step weekly Chlorophyll-a (Chla) prediction models: multiple linear regression (MLR), back propagation artificial neural network (BPANN) and support vector regression machine (SVR). Previously, five major environmental factors were selected as potential input variables of prediction models by correlation analysis and principal component analysis. The model training and validation point to that a low number of input variables are able to predict the Chla trends well by BPANN and SVR methods while MLR responses unacceptably. Additionally, the SVR machine presents the best performance in light of root mean square error and generalization ability. The sensitivity analysis indicates that the Chla in the next week is sensitive to the changes of Chla, water temperature. In conclusion, we choose SVR machine to be the most suitable model in this case.
Starting Page	1883
Ending Page	1886
File Size	343600
Page Count	4
File Format	PDF
ISBN	9781424494361
e-ISBN	9781424494392
DOI	10.1109/MACE.2011.5987332
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Chlorophyll-a Biological system modeling Artifical neural network Eutrophication prediction Artificial neural networks Predictive models Reservoirs Data models Support vector machine Multiple regression
Content Type	Text
Resource Type	Article

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