Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation. (December 2017)

• Record Type: Journal Article
• Authors: Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe
• Is Part Of: Environmental pollution. Volume 231:Part 1(2017)
• Page Start: 997
• Page End: 1004
• Publication Date: 2017-12
• Subjects: Air pollutant concentration predictions -- Long short-term memory neural network (LSTM NN) -- Recurrent neural network -- Spatiotemporal correlation -- Multiscale prediction; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2017.08.114
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%). Graphical abstract: Highlights: Regional air pollutant concentration shows an obvious spatiotemporal correlation. Our prediction model presents superior performance. Climate data and metadata can significantly improve the prediction performance. Abstract : This paper presents a high-accuracy model of air pollutant concentration prediction based on an LSTM neural network, and spatiotemporal correlations are inherently considered.