[1]李亦芳,程万里,刘建厅..基于人工神经网络与回归分析的水质预测[J].郑州大学学报(工学版),2008,29(01):106-109.
　LI Yifang,Cheng Wanli,Liu Jian Hall.Water quality prediction based on artificial neural network and regression analysis[J].Journal of Zhengzhou University (Engineering Science),2008,29(01):106-109.

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基于人工神经网络与回归分析的水质预测()

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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

29卷

期数:

2008年01期

页码:

106-109

栏目:

出版日期:

1900-01-01

文章信息/Info

Title:

Water quality prediction based on artificial neural network and regression analysis

作者:

李亦芳; 程万里; 刘建厅.

华北水利水电学院,数学与信息科学学院,河南,郑州,450011, 华北水利水电学院,数学与信息科学学院,河南,郑州,450011, 华北水利水电学院,数学与信息科学学院,河南,郑州,450011

Author(s):

LI Yifang; Cheng Wanli; Liu Jian Hall

关键词:

回归分析;  人工神经网络;  水质预测

Keywords:

regression analysis;  artificial neural networks;  Water quality forecasting

文献标志码:

A

摘要:

针对人工神经网络在预测中出现的异常值现象,采用了回归分析模型得到的预测区间来控制异常值现象的方法.并且应用在黄河三门峡河段的水质预测中,氨氮通量预测的网络模型控制前平均精度仅有50.05%,这是因为2006年6月份预测值偏离真实值太大,预测相对误差达到214.88%,超出了回归预测区间,从而影响了整体精度.控制后该月的相对精度为90.08%,平均精度达到80.79%,整体预测精度明显提高.实践表明,该方法对于消除网络模型预测中出现的异常值现象是较为有效的.

Abstract:

Aiming at the outlier phenomenon in the prediction of artificial neural network, the prediction interval obtained by the regression analysis model is used to control the outlier phenomenon. Moreover, applied to the water quality prediction of the Sanmenxia section of the Yellow River, the average accuracy of the network model for ammonia nitrogen flux prediction before control was only 50.05%, because the predicted value in June 2006 deviated too much from the real value, and the relative error of the prediction reached 6.214%, which exceeded the regression prediction interval, thus affecting the overall accuracy. After control, the relative accuracy of the month was 88.90%, and the average accuracy reached 08.80%, and the overall prediction accuracy was significantly improved. Practice shows that this method is more effective for eliminating outliers in network model prediction.

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