OPAQ系统两种模式对O<sub>3</sub>预报准确率的探讨

王淑莹<sup>1</sup>; 许纯领<sup>2*</sup>; 尹翠芳<sup>1</sup>; 李鹏帅<sup>1</sup>

引用本文:	王淑莹, 许纯领, 尹翠芳,李鹏帅.OPAQ系统两种模式对O₃预报准确率的探讨[J].环境监控与预警,2020,12(2):13-16
	WANG Shu-ying,XU Chun-ling , YIN Cui-fang, LI Peng-shuai.Discussion on the Accuracy of O₃ Prediction by Two Modules of OPAQ System[J].Environmental Monitoring and Forewarning,2020,12(2):13-16

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OPAQ系统两种模式对O₃预报准确率的探讨
王淑莹, 许纯领, 尹翠芳,李鹏帅^1,2
1.北京立博威拓环境技术有限公司, 北京 100085;2.江苏省宿迁环境监测中心,江苏宿迁 223800

摘要:

以江苏省宿迁环境监测中心OPAQ系统为例，基于人工神经网络算法的OPAQ空气质量预报系统的2种模式对O₃预报准确率的进行了分析，结果表明，趋势最优模式（RMSE模式）对预报当天及未来3d的预报值与监测值的相关性系数均>0.78，相对误差在25%以下，在预测当天及未来24、48及72h优-良天的预测准确率较高，分别为88.8%、87.2%、86.3%及84.7%，在预测轻度污染-重度污染的准确率较低；极值最优模式（SI模式）对预报当天及未来3 d的预报值与监测值的相关性系数(R)均>0.76，相对误差<32%，预测未来24和48 h的轻度污染-中度污染的级别准确率>60%。OPAQ系统的极值最优模式（SI模式）更适合作为夏季ρ（O₃）较高时的预测工具。

关键词: 人工神经网络算法空气质量预报业务系统 O₃ 预报准确率

DOI：

分类号:X839.2

文献标识码:B

基金项目:

Discussion on the Accuracy of O₃ Prediction by Two Modules of OPAQ System

WANG Shu-ying,XU Chun-ling , YIN Cui-fang, LI Peng-shuai^1,2

1. Beijing LIBOVITO Environmental Technology Company, Beijing 100085, China;2 Jiangsu Suqian Environmental Monitoring Center, Suqian，Jiangsu 223800, China

Abstract:

In this paper, two models of OPAQ air quality prediction system based on artificial neural network algorithm are used to analyze the accuracy of O₃ prediction. Taking the OPAQ system of Jiangsu Suqian Environmental Center as an example, the results show that the correlation coefficients R of RMSE model for predicting the current day and the next three days are above 0.78 and the relative errors are below 25%. The prediction accuracy of RMSE model for the current day and the 24 hour, 48 hour and 72 hour excellent good days is high, which are 88.8%, 87.2%, 86.3% and 84.7% respectively. The accuracy of RMSE model for predicting the light heavy pollution is low. The correlation coefficients R of the extreme optimal model (SI model) for predicting the current day and the next three days are above 0.76, and the relative errors are below 32%. The accuracy of predicting the level of mild moderate pollution in the next 24 hours and 48 hours is above 60%. In summary, the extreme optimal model (SI model) of OPAQ system is more suitable as a prediction tool when the concentration of O₃ is high in summer.

Key words: Neural network algorithm OPAQ system Ozone Forecast Accuracy