基于BP神经网络预测地表水净化装置总氮的去除效果

李春华; 胡文; 叶春; 李金泽; 魏伟伟

doi:10.3969/j.issn.1674-991X.2018.06.086

基于BP神经网络预测地表水净化装置总氮的去除效果

doi: 10.3969/j.issn.1674-991X.2018.06.086

李春华^1,,
胡文^1,2,
叶春^1,*, ,,
李金泽^1,3,
魏伟伟¹

1.
中国环境科学研究院,湖泊水污染治理与生态修复技术国家工程实验室,北京 100012
2.
贵州师范大学地理与环境科学学院,贵州贵阳 550025
3.
燕京理工学院建筑学院,河北廊坊 065201

详细信息

作者简介:
作者简介:李春华(1977—),女,副研究员,博士,主要从事湖泊富营养化控制及生态修复研究,lich@craes.org.cn

通讯作者:
叶春 E-mail: yechbj@163.com

中图分类号: X524;
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- 被引次数: 0
出版历程
- 收稿日期: 2018-03-12
- 刊出日期: 2018-11-20

Study on prediction of total nitrogen removal effect of a surface water purification device based on BP neural network

LI Chunhua^1
,,
HU Wen^1,2,
YE Chun^{1,*
, ,},
LI Jinze^1,3,
WEI Weiwei¹

1.
National Engineering Laboratory for Lake Pollution Control and Ecological Restoration, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
2.
School of Geographic and Environmental Science, Guizhou Normal University, Guiyang 550025, China
3.
School of Architecture, Yanching Institute of Technology, Langfang 065201, China

More Information

Corresponding author: 叶春 E-mail: yechbj@163.com

摘要

摘要: 为了模拟预测地表水净化装置脱氮效果,利用水质指标实测数据作为学习样本,选取原水总氮、氨氮、硝氮、COD_Mn及装置运行时间等指标作为预测参数,建立了BP神经网络水质预测模型,并运用该模型对净化装置的水质进行预测,同时引入多元线性回归模型作为对比。结果表明,BP神经网络模型预测值的可决系数为0.985,最大误差为5.92%,明显优于多元线性回归模型预测效果;BP神经网络模型预测精度较高,预测速度快,能够准确地预测净化装置的总氮去除效果。
- 地表水净化 /
- 脱氮 /
- 效果预测 /
- BP神经网络
Abstract: A back propagation (BP) artificial neural network model was set up to predict the effect of nitrogen removal using a surface water purification device. The observed data of water quality parameters were used as study sample, and the raw water TN, ammonium nitrogen, nitrate nitrogen, COD_Mn and operation time of the device selected as projection parameter in this model. Besides, the multivariate linear regression model was introduced to compare with BP neural network. The results showed that the coefficient of determination of BP artificial neural network model was 0.985, which stayed at a high level. And the maximum error was 5.92%. Obviously, BP artificial neural network model was more precise, faster and better than multivariate linear regression model. It could accurately predict the removal effect of TN by purification device.
- surface water purification /
- nitrogen removal /
- effect prediction /
- BP neural network

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参考文献(18)

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