| Prediction and Analyses of Residual Chlorine Based on Support Vector Regression in Urban Water Distribution System |
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| 作者姓名： | TIAN Yi - mei WU Mi - fang Wang Yang |
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| 作者单位： | The College of Environmental Science and Engineering, Tianjin University, Tianjin 300072, P. R. China |
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| 摘 要： | Support vector regression(SVR) algorithm is an application of structural risk minimization principle in function regression.In this paper,a residual chlorine prediction model based on SVR is established by using the data of manual sampling residual chlorine of water distribution system in a certain city in the north of China.SVR model is compared with the artificial neural network and multivariate linear regression.The result shows that SVR model has better generalization ability for small samples,the predicted average relative error of all monitoring points is 1.80%~8.73%,and can achieve unique and globally optimal solutions.It is practical and can solve the problem for small samples of residual chlorine when the fit precision of model is good but the predicted effect is worse.
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| 关 键 词： | Support Vector Regression water distribution system residual chlorine prediction modeling |
| 收稿时间： | 2005/12/30 0:00:00 |
| 修稿时间： | 2005/12/30 0:00:00 |
Prediction and Analyses of Residual Chlorine Based on Support Vector Regression in Urban Water Distribution System |
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| TIAN Yi - mei,WU Mi - fang,Wang Yang.Prediction and Analyses of Residual Chlorine Based on Support Vector Regression in Urban Water Distribution System[J].Storage & Process,2006(2):74-78. |
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| Authors: | TIAN Yi - mei WU Mi - fang Wang Yang |
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| Institution: | The College of Environmental Science and Engineering, Tianjin University, Tianjin 300072, P. R. China |
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| Abstract: | Support vector regression(SVR) algorithm is an application of structural risk minimization principle in function regression.In this paper,a residual chlorine prediction model based on SVR is established by using the data of manual sampling residual chlorine of water distribution system in a certain city in the north of China.SVR model is compared with the artificial neural network and multivariate linear regression.The result shows that SVR model has better generalization ability for small samples,the predicted average relative error of all monitoring points is 1.80%~8.73%,and can achieve unique and globally optimal solutions.It is practical and can solve the problem for small samples of residual chlorine when the fit precision of model is good but the predicted effect is worse. |
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| Keywords: | Support Vector Regression water distribution system residual chlorine prediction modeling |
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