Cluster Computing

, Volume 22, Supplement 5, pp 12481–12493 | Cite as

Performance evaluation of mathematical predictive modeling for air quality forecasting

  • S. SelviEmail author
  • M. Chandrasekaran


The extensive data has got more attention among researchers in the field of data analytics. The rate of growth of information in the area of information sciences exceeds to a more significant extent. And now it’s a significant challenge to deal this big data such as difficulties in data analysis, data capture, data storage and data visualization, etc. This paper illustrates to mine knowledge derived from the environmental issues. It is proposed to utilize the study of several processes of effects such as mitigation, various arrangements aimed at risk distribution. This paper briefs as to how to use data mining algorithms which are built on R programming tool. There are R packages open-air and ropenaq which had been developed for analyzing air pollution environment data. It is shown that how this package can effectively makes utilization of environmental data sets to derive patterns. These derived patterns can be then applied to study the environmental issues which are helpful to develop a predictive model. And this predictive model plays a vital role in decision-making which involves uncertainty. Hence, scientific representations take to develop the main feature of decision-making care in many procedure measures, especially those for some precautions from natural disasters.


Data mining algorithms Rough set theory R programming tool Big data and machine learning 


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringGovernment College of EngineeringBargurIndia
  2. 2.Department of Electronics and Communication EngineeringGovernment College of EngineeringBargurIndia

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