The Prediction of the Air Quality based on the Prophet Algorithm
DOI:
https://doi.org/10.54097/hset.v39i.6705Keywords:
Prophet Algorithm; Data Analysis; Air Quality Prediction.Abstract
Due to the fact that the environmental problems are increasingly focused and among them, air quality is closely related to human’s health. It is necessary to have a better understanding on the surrounding air quality. In this paper, a dataset collected from the internet is used to exemplify the usage of the Prophet algorithm on the prediction of air quality. Compared with the conventional monitor and analytical methods, it can not only record the up-to-date air quality indexes, but it can also do some predictions on the air quality based on the dataset and plot the trend. Since Prophet algorithm can take seasonalities and holiday effects into considerations, it is suitable to employ it to predict the changeable air quality. The prediction results were output by the codes in the form of line charts. According to the experimental results in this study, the accuracy of the model is acceptable and reliable.
Downloads
References
WANG J S, WANG Y, ZHAO M X, et al. Application of ARIMA model in the prediction of air quality index in Suzhou. Journal of Public Health and Preventive Medicine, 2019, 30(2):18-20.
Yang, S., and L. Zhao. Application of Random Forest Algorithm in Urban Air Quality Forecast. Stat. Decis 20, 2017, 83-86.
Chang Tianjun, et al. Prediction of air quality index size based on Prophet-Stochastic Forest Optimization model, Environmental Pollution and Prevention, 41.07, 2019.
S. De Vito, E. Massera, M. Piga, L. Martinotto, G. Di Francia, On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario, Sensors and Actuators B: Chemical, Volume 129, Issue 2, 22 February 2008, Pages 750-757, ISSN 0925-4005.
Gong, Feixiang, et al. Trend analysis of building power consumption based on prophet algorithm. 2020 Asia Energy and Electrical Engineering Symposium (AEEES). IEEE, 2020.
Zunic, Emir, et al. Application of facebook's prophet algorithm for successful sales forecasting based on real-world data. arXiv preprint arXiv:2005.07575, 2020.
Argue, C. J., et al. Robust Secretary and Prophet Algorithms for Packing Integer Programs. Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). Society for Industrial and Applied Mathematics, 2022.
Yu, Q et al. Improved denoising autoencoder for maritime image denoising and semantic segmentation of USV. China Communications 17.3, 2020, 46-57.
Monil, Patel, et al. Customer Segmentation Using Machine Learning. International Journal for Research in Applied Science and Engineering Technology (IJRASET) 8.6, 2020, 2104-2108.
Kourou, Konstantina, et al. Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal 13, 2015, 8-17.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
