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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Method to predict key factors affecting lake eutrophication: a new approach based on Support Vector Regression model. / Xu, Yunfeng; Ma, Chunzi; Liu, Qiang et al.
In: International Biodeterioration and Biodegradation, Vol. 102, 08.2015, p. 308-315.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Xu, Y, Ma, C, Liu, Q, Xi, B, Qian, G, Zhang, D & Huo, S 2015, 'Method to predict key factors affecting lake eutrophication: a new approach based on Support Vector Regression model', International Biodeterioration and Biodegradation, vol. 102, pp. 308-315. https://doi.org/10.1016/j.ibiod.2015.02.013

APA

Xu, Y., Ma, C., Liu, Q., Xi, B., Qian, G., Zhang, D., & Huo, S. (2015). Method to predict key factors affecting lake eutrophication: a new approach based on Support Vector Regression model. International Biodeterioration and Biodegradation, 102, 308-315. https://doi.org/10.1016/j.ibiod.2015.02.013

Vancouver

Xu Y, Ma C, Liu Q, Xi B, Qian G, Zhang D et al. Method to predict key factors affecting lake eutrophication: a new approach based on Support Vector Regression model. International Biodeterioration and Biodegradation. 2015 Aug;102:308-315. Epub 2015 Mar 5. doi: 10.1016/j.ibiod.2015.02.013

Author

Xu, Yunfeng ; Ma, Chunzi ; Liu, Qiang et al. / Method to predict key factors affecting lake eutrophication : a new approach based on Support Vector Regression model. In: International Biodeterioration and Biodegradation. 2015 ; Vol. 102. pp. 308-315.

Bibtex

@article{0245aead60204a42a8daca02d3bc9e16,
title = "Method to predict key factors affecting lake eutrophication: a new approach based on Support Vector Regression model",
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.",
keywords = "Chlorophyll-a, Environmental factors, Support vector regression, Total nitrogen, Total phosphorus",
author = "Yunfeng Xu and Chunzi Ma and Qiang Liu and Beidou Xi and Guangren Qian and Dayi Zhang and Shouliang Huo",
note = "Date of Acceptance: 12/02/2015",
year = "2015",
month = aug,
doi = "10.1016/j.ibiod.2015.02.013",
language = "English",
volume = "102",
pages = "308--315",
journal = "International Biodeterioration and Biodegradation",
issn = "0964-8305",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Method to predict key factors affecting lake eutrophication

T2 - a new approach based on Support Vector Regression model

AU - Xu, Yunfeng

AU - Ma, Chunzi

AU - Liu, Qiang

AU - Xi, Beidou

AU - Qian, Guangren

AU - Zhang, Dayi

AU - Huo, Shouliang

N1 - Date of Acceptance: 12/02/2015

PY - 2015/8

Y1 - 2015/8

N2 - 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.

AB - 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.

KW - Chlorophyll-a

KW - Environmental factors

KW - Support vector regression

KW - Total nitrogen

KW - Total phosphorus

U2 - 10.1016/j.ibiod.2015.02.013

DO - 10.1016/j.ibiod.2015.02.013

M3 - Journal article

AN - SCOPUS:84941314304

VL - 102

SP - 308

EP - 315

JO - International Biodeterioration and Biodegradation

JF - International Biodeterioration and Biodegradation

SN - 0964-8305

ER -