系统仿真学报 ›› 2017, Vol. 29 ›› Issue (1): 14-20.doi: 10.16182/j.issn1004731x.joss.201701003

• 仿真建模理论与方法 • 上一篇    下一篇

多元逐步与概率混合回归法在霾预报中的应用

谢永华1,2, 杨乐1, 张鸣敏1, 张恒德3   

  1. 1.南京信息工程大学计算机与软件学院,南京 210044;
    2.南京信息工程大学江苏省网络监控中心,南京 210044;
    3.中国气象局国家气象中心,北京 100081
  • 收稿日期:2015-04-24 修回日期:2015-06-19 出版日期:2017-01-08 发布日期:2020-06-01
  • 作者简介:谢永华(1976-),男,江苏靖江,博士,教授,研究方向为人工智能、图像处理。
  • 基金资助:
    国家自然科学基金(61375030),公益性行业(气象)科研专项基金项目(GYHY201306015)

Application of Haze Forecasts Based on Combined Multivariable and Probability Stepwise Regression

Xie Yonghua1,2, Yang Le1, Zhang Mingmin1, Zhang Hengde3   

  1. 1. School of Computer and Software, Nanjing University of Information science and Technology, Nanjing 210044, China;
    2. Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information and Technology, Nanjing 210044, China;
    3. Nation Meteorological Center of CMA, Beijing 100081, China
  • Received:2015-04-24 Revised:2015-06-19 Online:2017-01-08 Published:2020-06-01

摘要: 针对目前霾预报模型较少,预报准确率低等缺点,将统计预报与数值预报相结合,提出了基于多元逐步回归算法与概率混合回归的霾预报方法。利用多元逐步回归法控制影响因变量的物理因子,建立能见度预报方程,利用概率回归结合能见度、相对湿度等物理参量建立基于二值变量的霾预报模型。实验结果表明,与现有业务上主要运行的雾霾数值预报系统CUACE相比,提出的混合回归预报算法的预报准确率得到了显著提高。

关键词: 多元逐步, 概率回归, 预报模型, 能见度, 霾预报

Abstract: Aiming at the less models available and poor prediction performance of current haze forecasting, combining statistical forecast and numerical prediction, a new haze prediction model was proposed based on hybrid stepwise multivariable and probability regression. Multivariable stepwise regression was used to control physical factors which influenced depending variable and the equation for atmosphere visibility wasgenerated. A haze prediction model based on binary variables was established using probability regression combined with factors like visibility and relative humidity. Experimental results have proved that compared with the existing CUACE, which is one of the main digital haze forecasting system, the accuracy using the hybrid regression forecasting model suggested is obviously higher.

Key words: multivariable stepwise, probability regression, forecasting model, visibility, haze forecast

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