doi: 10.18178/ijesd.2018.9.1.1066
Air Quality Prediction: Big Data and Machine Learning Approaches
Gaganjot Kaur Kang1
,
Jerry Zeyu Gao2 , , ,
- 1the Department of Computer Engineering, San Jose State University, USA
- 2San Jose State University, USA
- 3University of Taiyuan University of Technology, China
- 4the Taiyuan University of Technology, China
- 5the Taiyuan University of Science and Technology, China
Abstract
Monitoring and preserving air quality has become one of the most essential activities in many industrial and urban areas today. The quality of air is adversely affected due to various forms of pollution caused by transportation, electricity, fuel uses etc. The deposition of harmful gases is creating a serious threat for the quality of life in smart cities. With increasing air pollution, we need to implement efficient air quality monitoring models which collect information about the concentration of air pollutants and provide assessment of air pollution in each area. Hence, air quality evaluation and prediction has become an important research area. The quality of air is affected by multi-dimensional factors including location, time, and uncertain variables. Recently, many researchers began to use the big data analytics approach due to advancements in big data applications and availability of environmental sensing networks and sensor data. The aim of this research paper is to investigate various big-data and machine learning based techniques for air quality forecasting. This paper reviews the published research results relating to air quality evaluation using methods of artificial intelligence, decision trees, deep learning etc. Furthermore, it throws light on some of the challenges and future research needs.
Keywords
- Air quality evaluation
- big data analytics
- data-driven air quality evaluation
- and air quality prediction
How to Cite
Gaganjot Kaur Kang, Jerry Zeyu Gao, Sen Chiao, Shengqiang Lu, and Gang Xie, "Air Quality Prediction: Big Data and Machine Learning Approaches," International Journal of Environmental Science and Development, vol. 9, no. 1, pp. 8-16, 2018. https://doi.org/10.18178/ijesd.2018.9.1.1066
Copyright & License
Copyright © 2018 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).