Abstract
Air pollution has become a global environmental problem, because it has a great adverse impact on human health and the climate. One way to explore this problem is to monitor and predict air quality index in an economical way. Accurate monitoring and prediction of air quality index (AQI), e.g., PM2.5 concentration, is a challenging task. In order to accurately predict the PM2.5 time series, we propose a supplementary leaky integrator echo state network (SLI-ESN) in this paper. It adds the historical state term of the historical moment to the calculation of leaky integrator reservoir, which improves the influence of historical evolution state on the current state. Considering the redundancy and correlation between multivariable time series, minimum redundancy maximum relevance (mRMR) feature selection method is introduced to reduce redundant and irrelevant information, and increase computation speed. A variety of evaluation indicators are used to assess the overall performance of the proposed method. The effectiveness of the proposed model is verified by the experiment of Beijing PM2.5 time series prediction. The comparison of learning time also shows the efficiency of the algorithm.
Highlights
With the rapid advancement of urbanization and industrialization, air quality has deteriorated severely, which has negatively affected the quality of the living environment and even hindered economic growth in some areas [1]
Phase space reconstruction is performed on the optimal subset to obtain a 14-dimensional reconstructed time series, which are used for inputs of prediction model
For the LI-echo state state network network (ESN) model, the reconstructed time series, which are used for inputs of prediction model
Summary
With the rapid advancement of urbanization and industrialization, air quality has deteriorated severely, which has negatively affected the quality of the living environment and even hindered economic growth in some areas [1]. The prediction of air pollutants plays a crucial role in the early warning and control of environmental pollution [2]. Modeling and forecasting the air quality index (e.g., PM2.5 concentration) has become an effective way to prevent and control air pollution, and it provides a scientific basis for the development of effective measures [3]. The implementation of this idea can effectively reduce the health hazard of air pollution, achieving early warning and rational planning [4]. All of them have been applied to predict air pollution concentration
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