A hybrid deep learning model with multi-source data for PM 2.5 concentration forecast
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A hybrid deep learning model with multi-source data for PM2.5 concentration forecast Qiang Sun1,2 · Yanmin Zhu1 · Xiaomin Chen1
· Ailan Xu3 · Xiaoyan Peng4
Received: 10 August 2020 / Accepted: 8 October 2020 © Springer Nature B.V. 2020
Abstract Air quality forecast is an important technical means to ensure timely and proper response to heavy pollution weather. In this study, a hybrid deep air quality predictor (HDAQP) model consisting of one-dimensional convolutional neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN) is proposed to forecast air quality indicators (mainly PM2.5 concentrations). The proposed model can overcome the limitations of the single model and meanwhile make the best of each. CNN model is used to convolve the historical PM2.5 concentration data along with meteorological data to extract shallow features, while LSTM model is used to extract the deep temporal features. Finally, the DNN model is adopted to transfer these deep features into the final forecast results. Compared with the mainstream deep learning models (e.g., RNN, LSTM, and CNN-LSTM models), the HDAQP model exhibits a better performance in short-term PM2.5 concentration forecast. With the increase of prediction time, the long-term prediction performance of the HDAQP model will be degraded, but it is still better than the mainstream deep learning models. Moreover, considering other meteorological factors, with the multi-source data, the HDAQP model can forecast PM2.5 concentrations more accurately. Keywords PM2.5 concentration forecast · Deep learning · Hybrid model · Multi-source data
Introduction With the acceleration of industrialization and urbanization progress, air pollution problem is becoming increasingly severe. According to the health report released by the World Health Organization (WHO) in 2019, air pollution is considered to be one of the top health threats. Air pollutants mainly include gaseous pollutants (e.g., CO, SO2 , NOx , O3 ) and granular pollutants (e.g., PM2.5 , PM10 ), which have a potentially negative impact on public life and even cause a series of health problems(Kampa and Castanas 2008; Liu
Xiaomin Chen
[email protected] 1
School of Information Science and Technology, Nantong University, Nantong 226019, China
2
Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China
3
Jiangsu Province Nantong Environmental Monitoring Centre, Nantong 226006, China
4
Nantong Meteorological Bureau, Nantong, 226006, China
et al. 2018; Wang et al. 2020). Methods against air pollution have gained significant attentions from both academic and social public service area. Among the common solutions, air quality forecast is an important technical means to ensure timely response to heavy pollution, in which PM2.5 is regarded as a critical factor for air quality. Existing air quality and PM2.5 concentration forecasting methods mainly include the numerical prediction models, statistical prediction models, machine learning (ML), and deep learning (DL) method
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