The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1451–1455, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1451-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1451–1455, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1451-2020

  22 Aug 2020

22 Aug 2020

PREDICTING DAILY PM2.5 USING WEIGHTED LONG SHORT-TERM MEMORY NEURAL NETWORK MODEL

H. Fan1, M. Yang1, F. Xiao2, and K. Zhao1 H. Fan et al.
  • 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 2School of Public Health, Southern Medical University, Guangzhou, 510515 Guangdong, China

Keywords: PM2.5 prediction; long short-term memory neural network; multi-layer perception; spatiotemporal dependency

Abstract. Over the past few decades, air pollution has caused serious damage on public health, thus making accurate predictions of PM2.5 crucial. Due to the transportation of air pollutants among areas, the PM2.5 concentration is strongly spatiotemporal correlated. However, the distribution of air pollution monitoring sites is not even, making the spatiotemporal correlation between the central site and surrounding sites varies with different density of sites, and this was neglected by most existing methods. To tackle this problem, this study proposed a weighted long short-term memory neural network extended model (WLSTME), which addressed the issue that how to consider the effect of the density of sites and wind condition on the spatiotemporal correlation of air pollution concentration. First, several the nearest surrounding sites were chosen as the neighbour sites to the central station, and their distance as well as their air pollution concentration and wind condition were input to multi-layer perception (MLP) to generate weighted historical PM2.5 time series data. Second, historical PM2.5 concentration of the central site and weighted PM2.5 series data of neighbour sites were input into LSTM to address spatiotemporal dependency simultaneously and extract spatiotemporal features. Finally, another MLP was utilized to integrate spatiotemporal features extracted above with the meteorological data of central site to generate the forecasts future PM_2.5 concentration of the central site. Daily PM_2.5 concentration and meteorological data on Beijing–Tianjin–Hebei from 2015 to 2017 were collected to train models and evaluate the performance. Experimental results with 3 other methods showed that the proposed WLSTME model has the lowest RMSE (40.67) and MAE (26.10) and the highest p (0.59). This finding confirms that WLSTME can significantly improve the PM2.5 prediction accuracy.