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Atmospheric PM2.5 Concentration Prediction Based on Time Series and Interactive Multiple Model Approach

机译：基于时间序列和交互式多模型方法的大气PM2.5浓度预测

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摘要

Urbanization, industrialization, and regional economic integration have developed rapidly in China in recent years. Air pollution has attracted more and more attention. However, PM2.5 is the main particulate matter in air pollution. Therefore, how to predict PM2.5 accurately and effectively has become a concern of experts and scholars. For the problem, atmosphere PM2.5 concentration prediction algorithm is proposed based on time series and interactive multiple model in this paper. PM2.5 concentration is collected by using the monitor at different air quality levels. The time series models are established by historical PM2.5 concentration data, which were given by the autoregressive model (AR). In the paper, three PM2.5 time series models are established for three different air quality levels. Then, the three models are converted to state equation, respectively, by autoregressive integrated with Kalman filter (AR-Kalman) approaches. Besides, the proposed interactive multiple model (IMM) algorithm is, respectively, compared with autoregressive (AR) model algorithm and AR-Kalman prediction algorithm. It is turned out the proposed IMM algorithm is more accurate than the other two approaches for PM2.5 prediction, and it is effective.
机译：近年来，中国在中国开发了城市化，产业化和区域经济一体化。空气污染吸引了越来越多的关注。然而，PM2.5是空气污染中的主要颗粒物质。因此，如何准确且有效地预测PM2.5已成为专家和学者的关注。对于问题，基于本文的时间序列和交互式多模型提出了大气PM2.5浓度预测算法。通过使用不同空气质量水平的显示器收集PM2.5浓度。时间序列模型由历史PM2.5浓度数据建立，由自回归模型（AR）给出。在论文中，建立了三个PM2.5时间序列模型，用于三种不同的空气质量水平。然后，通过与卡尔曼滤波器（AR-Kalman）方法进行自回归，分别将三种模型转换为状态等式。此外，与自回归（AR）模型算法和AR-Kalman预测算法相比，所提出的交互式多模型（IMM）算法分别是相比。结果原出，所提出的IMM算法比PM2.5预测的其他两种方法更准确，并且它是有效的。

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