| A BOD-DO coupling model for water quality simulation by artificial neural network |
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| 引用本文： | 郭劲松 LONG Tengrui 等. A BOD-DO coupling model for water quality simulation by artificial neural network[J]. 重庆大学学报(英文版), 2002, 1(2): 46-49 |
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| 作者姓名： | 郭劲松 LONG Tengrui 等 |
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| 作者单位： | CollegeofUrbanConstructionandEnvironmentEngineering,ChongqingUniversity,Chongqin400045,P.R.China |
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| 基金项目： | Funded by the National Natural Science Foundation of China (No.59838300，No.59778021) |
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| 摘 要： | A one-dimensional BOD-DO coupling model for water quality simulation is presented,which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network.The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model.The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.
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| 关 键 词： | 水质模拟 人工神经网络 BP算法 BOD-DO耦合模型 水质评价 |
A BOD-DO coupling model for water quality simulation by artificial neural network |
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| GUO Jingsong,LONG Tengrui,HE Guoyou College of Urban Construction and Environment Engineering,Chongqing University,Chongqing,P. R. China. A BOD-DO coupling model for water quality simulation by artificial neural network[J]. Journal of Chongqing University(English Edition), 2002, 1(2): 46-49 |
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| Authors: | GUO Jingsong LONG Tengrui HE Guoyou College of Urban Construction Environment Engineering Chongqing University Chongqing P. R. China |
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| Affiliation: | GUO Jingsong,LONG Tengrui,HE Guoyou College of Urban Construction and Environment Engineering,Chongqing University,Chongqing 400045,P. R. China |
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| Abstract: | A one-dimensional BOD-DO coupling model for water quality simulation is presented, which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network. The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model. The result shows that such model is feasible for water quality simulation and is more accurate than traditional models. |
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| Keywords: | water quality simulation artificial neural network B-P algorithm |
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