Air quality prediction using CT-LSTM
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S.I. : HIGHER LEVEL ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT SYSTEMS
Air quality prediction using CT-LSTM Jingyang Wang1 • Jiazheng Li1 • Xiaoxiao Wang1 • Jue Wang2 • Min Huang1 Received: 29 August 2020 / Accepted: 11 November 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract With the development of industry, air pollution has become a serious problem. It is very important to create an air quality prediction model with high accuracy and good performance. Therefore, a new method of CT-LSTM is proposed in this paper, in which the prediction model is established by combining chi-square test (CT) and long short-term memory (LSTM) network model. CT is used to determine the influencing factors of air quality. The hourly air quality data and meteorological data from Jan. 1, 2017 to Dec. 31, 2018 are used to train the LSTM network model. The data from Jan. 1, 2019 to Dec. 31, 2019 are used to evaluate the LSTM network model. The AQI level of Shijiazhuang of Hebei Province of China from Jan. 1, 2019 to Dec. 31, 2019 is predicted with five methods (SVR, MLP, BP neural network, Simple RNN and this paper’s new method). Then, a contrastive analysis of the five prediction results is made. The experimental results show that the accuracy of this new method reaches 93.7%, which is the highest in the five methods and the maximum error of this new method is 1. The correct number of days predicted by this new method is also the highest among the five methods, which is 342 days. The new method also shows good characteristics in MAE, MSE and RMSE, which makes it more accurate for people to predict the AQI level. Keywords LSTM Chi-square test Air quality Prediction
1 Introduction In recent years, with the rapid development of urbanization and industrialization and the intensification of human activities, energy consumption is increasing. This leads to more and more serious environmental pollution problems. As the main pollutant killers, PM2:5 ,PM10 ,SO2 and other air pollutants not only make the environment worse, but they are also a serious threat to human health. Air quality has gradually become a hot issue of people’s daily concerns [1, 2].
& Min Huang [email protected] 1
School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
2
College of Computer Science and Engineering, Northeastern University, Shenyang 110000, China
The air quality index (AQI) indicates the level of air pollution. It is affected by the concentration of various pollutants in the air. One of the factors affecting air quality comes from the emission of man-made pollutants, including motor vehicle exhaust, factory waste, residential heating, waste burning and so on. Many pollutants in the air are harmful to human health. Such pollutants include carbon monoxide (CO), particulate matters (e.g., PM2:5 and PM10 ), ozone (O3 ), nitrogen dioxide (NO2 ) and sulfur dioxide (SO2 ). These pollutants are the main influencers of t
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