Research on Prediction Model of Xiamen Air Quality Index Based on Machine Learning
Authors: 
Nan Chen, Shuang YangAuthors Info & Claims
Nan Chen, Shuang YangAuthors Info & ClaimsPages 1779 - 1784
Abstract
Air quality is an environmental issue that everyone must take seriously. Establishing an Air Quality Index (AQI) prediction model, and predicting AQI timely can help ensure people's quality of life and maintain sustainable development of the society. Based on the hourly air quality data of Xiamen City from January 1, 2020 to December 31, 2021, this topic firstly uses grey relational analysis (GRA) to analyze the factors affecting air quality. Secondly, establish the Random Forest, XGBoost, RNN, and LSTM models to predict AQI, and ues the genetic algorithm to optimize the number of hidden layers, Dense layers and the number of neurons in the LSTM model to obtain the GA-LSTM model. The result shows that the GA-LSTM model has high prediction accuracy (MSE=13.048, RMSE=3.612, MAE=2.350, R2=0.915).
References
[1]
W. Mao, W. Wang, L. Jiao, S. Zhao, and A. Liu, “Modeling air quality prediction using a deep learning approach: Method optimization and evaluation,” Sustainable Cities and Society, vol. 65, p. 102567, 2021.
[2]
r. Jin, g. Zeng, and Wangsha, “Research on air quality prediction based on neural network model,” Heilongjiang Science, 2021.
[3]
Wangqian, j. Wu, and y. Lin, “Application test of cmaq model and its correction technology in pm2.5 forecast in shanghai,” Journal of Environmental Science, no. 6, pp. 1651–1656, 2015.
[4]
j. He, Yuye, Liuna, s. Zhao, and j. Chen, “Prediction model of autumn and winter air pollution in lanzhou based on wrf model,” meteorological,vol. 39, no. 10, pp. 1293–1303, 2013.
[5]
S. Yang and L. Zhao, “Application of stochastic forest algorithm in urban air quality prediction,” statistics and decision, no. 20, pp. 83–86, 2017.
[6]
LiuXia and C. He, “Prediction of beijing air quality based on machine learning,” information record material, vol. 22, no. 10, p. 4, 2021.
[7]
L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.
[8]
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794, 2016.
[9]
R. Yan, J. Liao, J. Yang, W. Sun, M. Nong, and F. Li, “Multi-hour and multi-site air quality index forecasting in beijing using cnn, lstm, cnnlstm, and spatiotemporal clustering,” Expert Systems with Applications, vol. 169, p. 114513, 2021.
[10]
Z. Dongwen, Z. Qi, X. Yunfeng, “Air quality prediction based on neural network model of long short-term memory,” Journal of Hebei University of Science and Technology, vol. 41, no. 1, pp. 67–75, 2020.
[11]
J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1992.
[12]
Q. Zhang, “Research on the application of genetic algorithms in optimization problems,” Journal of Shanxi Normal University: Natural Science edition, vol. 28, no. 1, pp. 38–42, 2014.
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- Research on Prediction Model of Xiamen Air Quality Index Based on Machine Learning
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Published In
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
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Association for Computing Machinery
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Published: 15 March 2023
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EITCE 2022
EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 21 - 23, 2022
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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