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