Acta Geodaetica et Cartographica Sinica ›› 2022, Vol. 51 ›› Issue (3): 361-372.doi: 10.11947/j.AGCS.2022.20200385

• Cartography and Geoinformation • Previous Articles     Next Articles

Continuous spatial coverage PM2.5 concentration forecast in China based on deep learning

MAO Wenjing1, WANG Weilin1, JIAO Limin1, LIU Anbao2   

  1. 1. School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, China;
    2. Fujian Jingwei Surveying Information Co., Ltd., Fuzhou 350001, China
  • Received:2020-08-13 Revised:2021-05-08 Published:2022-03-30
  • Supported by:
    The National Natural Science Foundation of China (No. 41971368); The National Key Research and Development Program of China (No. 2017YFA0604404)

Abstract: To achieve real-time and high-precision spatiotemporal forecast of PM2.5 concentration in China with spatial coverage is still a difficult problem. In this paper, two models based on deep learning were established to realize the spatiotemporal forecast in the hourly scale of PM2.5 concentration in China:a multi-layer long and short-term memory iterative model and an improved spatial back-propagation neural network (S-BPNN) model. First of all, we based on spatial correlation to divide 1286 air quality monitoring stations across the country in space adaptively and built a multi-layer LSTM iterative model for each region to achieve the PM2.5 concentration forecast of each monitoring site in the next 24 hours. Secondly, the improved S-BPNN spatialization model was applied to realize the refined mapping of PM2.5 concentration in a large continuous spatial coverage across the country in the next 24 hours. We integrated historical data of PM2.5 monitoring stations in China from 2016 to 2019 for training and verification. The results show that the correlation coefficients R2 of the forecast model and the spatialized model are respectively 0.88 and 0.89, indicating that the two models could achieve high accuracy. Finally, based on the proposed two models and related data crawled from monitoring stations in real-time, the intelligent online information prototype system for air pollutant concentration forecast can be built to release the forecast results of stations in real-time and display spatial results. The study has realized the real-time prediction of PM2.5 concentration with high spatio-temporal accuracy across the country and has strongly supported joint prevention and control of air pollution and public environmental spatial quality information services.

Key words: air pollution, spatio-temporal forecast, PM2.5, deep learning

CLC Number: