OPAQ系统两种模式对O3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

【打印本页】【HTML】【下载PDF全文】【查看/发表评论】【EndNote】【RefMan】【BibTex】

←前一篇|后一篇→

过刊浏览高级检索

本文已被：浏览 896次下载 651次	码上扫一扫！
分享到：微信更多字体:加大+\|默认\|缩小-
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

作者	单位
王淑莹¹, 许纯领^2*, 尹翠芳¹,李鹏帅¹	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

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