Abstract
The recent bout of increased air pollution in Delhi has just made the task of identifying and controlling the causes of air pollution an extremely crucial task. In this paper, an Apriori-based association rule mining algorithm, which is a modified version of the Continuous Target Sequential Pattern Discovery (CTSPD), is used to generate a set of association rules that help in predicting the concentration of air pollutants. This algorithm considers the temporal aspect of the data and hence gives the rules with continuous events only as the result. The performance of the algorithm is evaluated by mining the air quality and meteorological data from Anand Vihar, New Delhi, over the period September 1, 2015 to August 31, 2016. The prediction of the proposed algorithm is compared with that of an existing prediction system, SAFAR and found that the proposed algorithm is more accurate than SAFAR.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Athanasiadis IN, Karatzas KD, Mitkas PA (2006) Classification techniques for air quality forecasting. In: Fifth ECAI workshop on binding environmental sciences and artificial intelligence, 17th European conference on artificial intelligence
Niharika, Venkatadri M, Rao PS (2014) A survey on air quality forecasting techniques. Int J Comput Sci Inf Technol 5(1):103–107
Kurt A, Gulbagci B, Karaca F, Alagha O (2008) An online air pollution forecasting system using neural networks. Environ Int 34(5):592–598
Kandya A, Mohan M (2009) Forecasting the urban air quality using various statistical techniques. In: Proceedings of the 7th international conference on urban climate
Peace M, Dirks K, Austin G (2005) 5.24 The prediction of air pollution using a site optimized model and mesoscale model wind forecasts
Kumar A, Goyal P (2014) Air quality prediction of PM10 through an analytical dispersion model for Delhi. Aerosol Air Qual Res 14:1487–1499
Ababneh MF, Ala’a O, Btoush MH (2014) PM10 forecasting using soft computing techniques. Res J Appl Sci Eng Technol 7(16):3253–3265
Zhu JY, Zheng Y, Yi X, Li VO (2016) A Gaussian Bayesian model to identify spatio-temporal causalities for air pollution based on urban big data. In: 2016 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, pp 3–8
Gudes E, Marina L (November 2004) Discovering target event rules based on time-consecutive pattern mining. In: 4th IEEE conference on data mining, Brighton, UK
DPCC Real Time Ambient Air Quality Data. http://www.dpccairdata.com/dpccairdata/display/index.php
SAFAR India. http://safar.tropmet.res.in/index.php?menu_id=1
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yadav, M., Jain, S., Seeja, K.R. (2019). Prediction of Air Quality Using Time Series Data Mining. In: Bhattacharyya, S., Hassanien, A., Gupta, D., Khanna, A., Pan, I. (eds) International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems, vol 56. Springer, Singapore. https://doi.org/10.1007/978-981-13-2354-6_2
Download citation
DOI: https://doi.org/10.1007/978-981-13-2354-6_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2353-9
Online ISBN: 978-981-13-2354-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)