Method to predict key factors affecting lake eutrophication:a new approach based on Support Vector Regression model

Xu, Yunfeng and Ma, Chunzi and Liu, Qiang and Xi, Beidou and Qian, Guangren and Zhang, Dayi and Huo, Shouliang (2015) Method to predict key factors affecting lake eutrophication:a new approach based on Support Vector Regression model. International Biodeterioration and Biodegradation, 102. pp. 308-315. ISSN 0964-8305

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Abstract

Developing quantitative relationship between environmental factors and eutrophic indices: chlorophyll-a (Chl-a), total nitrogen (TN) and total phosphorus (TP), is highly desired for lake management to prevent eutrophication. In this paper, Support Vector Regression model (SVR) was introduced to fulfill this purpose and the obtained result was compared with previous developed model, back propagation artificial neural network (BP-ANN). Results indicate SVR is more effective for the predication of Chl-a, TN and TP concentrations with less mean relative error (MRE) compared with BP-ANN. The optimal kernel function of SVR model was identified as RBF function. With optimized C and ε obtained in training process, SVR could successfully predict Chl-a, TN and TP concentrations in Chaohu lake based on other environmental factors observation.

Item Type:
Journal Article
Journal or Publication Title:
International Biodeterioration and Biodegradation
Additional Information:
Date of Acceptance: 12/02/2015
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2500/2502
Subjects:
ID Code:
75676
Deposited By:
Deposited On:
28 Sep 2015 11:05
Refereed?:
Yes
Published?:
Published
Last Modified:
08 Jul 2020 04:51