| Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow |
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| 引用本文： | 张旋,王启山,于淼,吴京.Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow[J].天津大学学报(英文版),2010,16(6):467-472. |
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| 作者姓名： | 张旋 王启山 于淼 吴京 |
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| 摘 要： | In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October.
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| 关 键 词： | principal component regression artificial neural network hybrid method chlorophyll-a eutrophica-tion |
| 收稿时间： | 4 February 2010 |
Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir's Outflow |
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| ZHANG Xuan,WANG Qishan,YU Miao,WU Jing.Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir's Outflow[J].Transactions of Tianjin University,2010,16(6):467-472. |
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| Authors: | ZHANG Xuan WANG Qishan YU Miao WU Jing |
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| Institution: | 1. College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China;School of Light Industry and Environmental Engineering, Shandong Institute of Light Industry, Jinan 250353, China
2. College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China |
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| Abstract: | In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression
(PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow.
The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely
chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were
utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal
component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation
coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results
show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied
to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with
the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October. |
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| Keywords: | principal component regression artificial neural network hybrid method chlorophyll-a eutrophica-tion |
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