基于广义隐马尔可夫模型的PM2.5浓度预测

doi:10.11949/j.issn.0438-1157.20171113

化工学报 ›› 2018, Vol. 69 ›› Issue (3): 1215-1220.DOI: 10.11949/j.issn.0438-1157.20171113

基于广义隐马尔可夫模型的PM_2.5浓度预测

张浩¹, 于君毅¹, 刘晓慧^2,3, 雷洪¹

1 西南大学化学化工学院, 重庆 400715;
2 南京信息工程大学大气物理学院, 江苏南京 210044;
3 河北省环境气象中心, 河北石家庄 050021

收稿日期:2017-08-15 修回日期:2017-08-22 出版日期:2018-03-05 发布日期:2018-03-05
通讯作者: 张浩
基金资助:
西南大学博士基金项目（SWU114021）；西南大学基本科研业务费（XDJK2015C101）。

Prediction of fine particulate matter concentrations based on generalized hidden Markov model

ZHANG Hao¹, YU Junyi¹, LIU Xiaohui^2,3, LEI Hong¹

1 School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China;
2 College of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China;
3 Hebei Provincial Environmental Meteorology Center, Shijiazhuang 050021, Hebei, China

Received:2017-08-15 Revised:2017-08-22 Online:2018-03-05 Published:2018-03-05
Supported by:
supported by the Doctoral Fund of Southwest University (SWU114021) and the Fundamental Research Funds for Southwest University (XDJK2015C101).

摘要/Abstract

摘要：

近年来，以PM_2.5为主要污染物的重霾污染事件频频发生，给我国国民经济和居民健康造成了严重损失。在空气质量尚未得到根本性改善的情况下，对重霾污染的准确预警不仅能使公众合理回避污染危害，还能为政府实施应急管理提供时间裕量。针对影响PM_2.5浓度的前体物及气象因素的非高斯分布特点以及传统隐马尔可夫模型（hidden Markov model，HMM）必须已知隐含状态个数的缺点，采用广义隐马尔可夫模型（generalized hidden Markov model，GHMM）对北京市除去定陵外的11个国控站点2013年1月～2017年1月的PM_2.5浓度进行了预测。结果表明：GHMM对严重污染及以上PM_2.5样本浓度预测准确率显著高于传统连续HMM，但针对中度污染及以下PM_2.5样本浓度的预测准确率接近传统HMM。

关键词: 系统工程, 环境, 污染, PM_2.5, 预测, 算法, 广义隐马尔可夫模型

Abstract:

In recent years, severe haze pollution accidents occurred frequently, which caused heavy losses in national economy and health of residents in China. Accurate early warning of severe haze pollution episodes can not only remind people of refraining from the hazards, but also set aside enough time for the government of emergency management before substantial improvement of air quality. According to the non-Gaussian distribution characteristics of PM_2.5 precursors and meteorological factors and limitation of the number of known hidden states in traditional hidden Markov models (HMMs), generalized hidden Markov models (GHMMs) were employed to make predictions of PM_2.5 concentrations at 11 nationally controlled monitoring sites (except Dingling site) in Beijing from January 2013 to January 2017. Data from Jan. 2013 to Dec. 2015 was used to train the GHMM models and data from Jan. 2016 to Jan. 2017 was used to validate these models. The same data was also used to train traditional HMM which contained 2 hidden states and 6 Gaussian distributions to make comparison with GHMM. Results illustrate that true prediction rate of GHMMs is significantly higher than that of traditional HMMs when applied on the prediction of samples over 250 μg·m⁻³; while both GHMMs and HMMs have similar performances when applied on the prediction of samples blow 150 μg·m⁻³.

Key words: systems engineering, environment, pollution, fine particulate matter, prediction, algorithm, generalized hidden Markov model

中图分类号:

X513

张浩, 于君毅, 刘晓慧, 雷洪. 基于广义隐马尔可夫模型的PM_2.5浓度预测[J]. 化工学报, 2018, 69(3): 1215-1220.

ZHANG Hao, YU Junyi, LIU Xiaohui, LEI Hong. Prediction of fine particulate matter concentrations based on generalized hidden Markov model[J]. CIESC Journal, 2018, 69(3): 1215-1220.

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基于广义隐马尔可夫模型的PM_2.5浓度预测

Prediction of fine particulate matter concentrations based on generalized hidden Markov model

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