基于广义隐马尔可夫模型的PM2.5浓度预测

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。

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

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.