AIR QUALITY INDEX USING MACHINE LEARNING – A JORDAN CASE STUDY
Predicting changes in air pollutant concentrations due to human and nature drivers are critical and challenging, particularly in areas with scant data inputs and high variability of parameters. This paper builds an Air Quality Index (AQI) model using Machine Learning algorithms and techniques. The paper employs Machine Learning Algorithms such as Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Random Forest (RF) and Logistic Regression. The model can predict the most pollutant factors from real readings published daily by the Jordan Ministry of Environment (MoEnv) for the period from January 2017 to April 2019. Jordan has prioritized air quality problems by establishing detection and monitoring stations in 12 positions across the country to measure Air Quality (AQ). Pollutant concentrations recorded by MoEnv use fully forewarn official organizations and individuals of daily air quality in the atmosphere over time and beneficially used by health and climate studies organizations. The study has detected the most contaminated sites and determined the pollutant concentrations. These estimates will indicate the most influenced pollutants and their behavior in the pollution process for further recommendations and actions to effects cardiopulmonary patients, environmental and climate researches, as well as to vulnerable ecosystems.
. I. Ungváriet al., “Relationship between air pollution, NFE2L2 gene polymorphisms and childhood asthma in a Hungarian population,” Journal of community genetics, vol. 3, no. 1, pp. 25–33, 2012.
. J. Kotcher, E. Maibach, and W.T. Choi, “Fossil fuels are harming our brains: identifying key messages about the health effects of air pollution from fossil fuels,” BMC public health, vol. 19, no. 1, p. 1079, 2019.
. D. A. Vallero, Fundamentals of air pollution. Academic press, Massachusetts, 2014.
. D. P. van Vuuren et al., “The representative concentration pathways: an overview,” Climatic change, vol. 109, no. 1–2, p. 5, 2011.
. MoEnv, “Annual Air Quality Report,” Amman, 2017.
. P. Hajek and V. Olej, “Predicting common air quality index--The case of Czech Microregions,” Aerosol and Air Quality Research, vol. 15, no. 2, pp. 544–555, 2015.
. P. Ghosh, “The Impact of Data Quality in the Machine Learning Era,” DataVersity, 2018.
. A. A. Al-Hasaan, T. F. Dann, and P. F. Brunet, “Air pollution monitoring in Amman, Jordan,” Journal of the Air & Waste Management Association, vol. 42, no. 6, pp. 814–816, 1992.
. M. Yang and J. A. de Loera, “A Machine Learning Approach to Evaluate Beijing Air Quality,” 2018.
. K. Veljanovskal and A. Dimoski, “Air quality index prediction using simple machine learning algorithms,” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 2018.
. H. Peng, “Air quality prediction by machine learning methods”, University of British Columbia, 2015. DOI: 10.14288/1.0166787
. D. Zhu, C. Cai, T. Yang, and X. Zhou, “A machine learning approach for air quality prediction: Model regularization and optimization,” Big data and cognitive computing, vol. 2, no. 1, p. 5, 2018.
. P.-W. Soh, J.-W. Chang, and J.-W. Huang, “Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations,” IEEE Access, vol. 6, pp. 38186–38199, 2018.
. H. Wang, C. Tseng, and T. Hsieh, “Developing an indoor air quality index system based on the health risk assessment,” Proceedings of indoor air, 2008.
. A. Liaw, M. Wiener, and others, “Classification and regression by random Forest,” R news, vol. 2, no. 3, pp. 18–22, 2002.
. D. G. Kleinbaum, K. Dietz, M. Gail, M. Klein, and M. Klein, Logistic regression. Springer, 2002.
. K. Nahar, A. Jaradat, M. Atoum, and F. Ibrahim, “Sentiment analysis and classification of arab jordanian facebook comments for jordanian telecom companies using lexicon-based approach and machine learning,” Jordanian J. Comput. Inf. Technol., vol. 6, no. 03, pp. 247–263, 2020.
. K. NAHAR, R. Khatib, M. Shannaq, and M. Barhoush, “An efficient holy quran recitation recognizer based on svm learning model,” Jordanian J. Comput. Inf. Technol., vol. 6, no. 04, pp. 392–414, 2020.
. “http://www.moenv.gov.jo/EN/List/Daily_Rates_OF_Air_Pollutants”, 2019. [Last Accessed on May 16, 2020]
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