Abstract
In recent years, Air Quality Index (AQI) have been widely used to describe the severity of haze and other air pollutions yet suffers from inefficiency and compatibility on real-time perception and prediction. In this paper, an Auto-Regressive (AR) prediction model based on sensed AQI values is proposed, where an adaptive Kalman Filtering (KF) approach is fitted to achieve efficient prediction of the AQI values. The AQI values were collected monthly from January 2018 to March 2019 using a WSN-based network, whereas daily AQI values started to be collected from October 1, 2018 to March 31, 2019. These data have been used for creation and evaluation purposes on the prediction model. According to the results, predicted values have shown high accuracy compared with the actual sensed values. In addition, when monthly AQI values were used, it has depicted higher accuracy compared to the daily ones depending on the experimental results. Therefore, the hybrid AR-KF model is accurate and effective in predicting haze weather, which has practical significance and potential value.
Highlights
In recent years, haze pollution has raised great concern in worldwide societies and scientific communities, due to its influencing living environment of human beings, even as potential impedance of the social progress from the world economic development perspective
The results have shown that the Kalman filter based on the AR model can be well fitted into the Air Quality Index (AQI) series data retrieved in Nanjing compared with individual model
It can be combined with other prediction models, such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) models to realize the hybrid prediction of the AQI in future
Summary
Haze pollution has raised great concern in worldwide societies and scientific communities, due to its influencing living environment of human beings, even as potential impedance of the social progress from the world economic development perspective. Different data retrieval methods have been used from historical monitoring results to WSN-based collection [3], as well as its optimization [4], [5]. Such work can be concluded into two groups including deterministic approaches and statistical approaches The former approaches focus on the physical theory in atmosphere and meteorological processes with concern on high-volume historical data, so diffusion models of the atmospheric pollution were generally presented by using specific mathematical approaches. Chen et al.: Adaptive KF Approach to Sensing and Predicting AQI Values proposed a WRF–Chem model with optimal parameters On this basis, a forecasting system was developed in order to describe air quality and meteorological measurements. The last section makes a conclusion of this paper
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