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Stratified Prediction of the Air Pollution Index using Operational Intelligence
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 14 |
| Year of Publication: 2023 |
| Authors: Alaa Ibrahem, Heba Elbeh, Hamdy M. Mousa |
10.5120/ijca2023922836
|
Alaa Ibrahem, Heba Elbeh, Hamdy M. Mousa . Stratified Prediction of the Air Pollution Index using Operational Intelligence. International Journal of Computer Applications. 185, 14 ( Jun 2023), 62-67. DOI=10.5120/ijca2023922836
Abstract
Our daily choices and lifestyle can affect the decrease or increase in air pollution, the overall health index, and the ozone layer. Unfortunately, ozone damage leads to the loss of 121 million tons of crops worldwide. In addition, the percentage of particulate matter (PM2.5) and surface ozone levels exceeded the limits set by the World Health Organization (WHO). The particles threatened most parts of Asia, Africa, and South America, causing sudden smog and lung disease. The Chinese central government has taken measures to improve the Air Quality Index (AQI) in Beijing and other regions. Measures have been taken to reduce primary emissions of gases and particulate matter. This study aimed to make a class prediction of the air quality index category in the Beijing region based on ozone and PM2.5 concentrations. The Internet of Things (IoT) has been used to collect sensor air quality data. The Internet of Things monitors air pollution and calculates levels of harmful gases, particulate matter, smoke, and more. This study focused on the use of operational intelligence in developing machine learning models for air quality index classification and comparing the results of classification models between the random forest classifier and the decision tree classifier. It helps in processing large amounts of data and making effective decisions. The random forest classifier has proven its efficiency in predicting the level of the air quality index. Temporal and spatial characteristics for the effective prediction of AQI were also considered.
References
- H. Aly, M. Elmogy, and S. Barakat, “Big Data on Internet of Things: Applications, Architecture, Technologies, Techniques, and Future Directions,” Int. J. Comput. Sci. Eng., vol. 4, no. 6, pp. 300–3013, 2015.
- E. K. Juarez and M. R. Petersen, “A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi,” Atmosphere (Basel)., vol. 13, no. 1, pp. 1–26, 2022.
- K. Maaloul and L. Brahim, “Comparative Analysis of Machine Learning for Predicting Air Quality in Smart Cities,” Wseas Trans. Comput., vol. 21, no. July, pp. 248–256, 2022.
- H. Alkabbani, A. Ramadan, Q. Zhu, and A. Elkamel, “An Improved Air Quality Index Machine Learning-Based Forecasting with Multivariate Data Imputation Approach,” Atmosphere (Basel)., vol. 13, no. 7, 2022.
- F. Hamami and I. A. Dahlan, “Air Quality Classification in Urban Environment using Machine Learning Approach,” IOP Conf. Ser. Earth Environ. Sci., vol. 986, no. 1, 2022
- R. M. Fernando, W. Ilmini, and D. U. Vidanagama, “Prediction of Air Quality Index in Colombo,” pp. 14–20, 2022.
- Y. Wang et al., “Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017,” Natl. Sci. Rev., vol. 7, no. 8, pp. 1331–1339, 2020, doi: 10.1093/nsr/nwaa032.
- Epa, U. S. "Technical assistance document for the reporting of daily air quality–The Air Quality Index (AQI)." Environmental Protection Agency (2018).
- United States Environmental Protection Agency. "Ground-level ozone basics."
- Environmental Protection Agency. "Particulate matter (PM) basics." (2021).
- Zaki, M. J., & Meira, Jr, W. (2018). Decision Tree Classifier. In Data Mining and Analysis (pp. 481–497).
- Baumann, F., Li, F., Ehlers, A., & Rosenhahn, B. (2014). Thresholding a Random Forest classifier. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8888, 95–106.
- World Health Organization. "WHO Air quality guidelines-World Health Organization." (2019).
- P. Zadrozny, K. Raghu,”. Big Data Analytics Using Splunk: Deriving Operational Intelligence from social media, Machine Data, Existing Data Warehouses, and Other Real-Time Streaming Sources; Apress,” New York, NY, USA, p. 376, 2013.
- S. Fedushko, T. Ustyianovych, and M. Gregus, “Real-time high-load infrastructure transaction status output prediction using operational intelligence and big data technologies,” Electron., vol. 9, no. 4, 2020.
- S. Messan, A. Shahud, A. Anis, R. Kalam, S. Ali, and M. I. Aslam, “Air-MIT: Air Quality Monitoring Using Internet of Things †,” Eng. Proc., vol. 20, no. 1, 2022.
- G. K. Kang, J. Z. Gao, S. Chiao, S. Lu, and G. Xie, “Air Quality Prediction: Big Data and Machine Learning Approaches,” Int. J. Environ. Sci. Dev., vol. 9, no. 1, pp. 8–16, 2018.
- C. L. Philip Chen and C. Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Inf. Sci. (Ny)., vol. 275, pp. 314–347, 2014, doi: 10.1016/j.ins.2014.01.015.
- Basics, A. Q. I. "AirNow. gov." AirNow. gov, US EPA, nd https://www. airnow. gov/aqi/aqi-basics.
- M. L. Toolkit, “MACHINE LEARNING (ML) | QUICK REFERENCE GUIDE Algorithm Examples,” no. Ml, 2017.
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