Urban Air Computing: For Air Quality Detection (International Journal of Innovative Research in Computer Science & Technology, 2019)
Air pollution is one of the biggest challenges  that every metro-political areas is facing today. There  are several tools and techniques evolved to predict  pollution level which in turn helps in controlling and  mitigating pollution. The three areas for computing  pollution level are feature analysis, interpolation and  prediction of fine grained air quality. These areas are  providing extremely useful information so that one can  take steps to mitigate pollution level, thus it also  generates big societal impacts. Currently, there are  individual models to address these issues separately.  This paper proposes proposes single efficient framework  by combining interpolation, prediction and feature  analysis for air quality detection. This framework  evaluates the different machine learning approaches to  predict the air pollution components based on real data  sets obtained from Bangalore. The main idea of the  Urban Air Computing(UAC) is to gather the data of air  quality and showing the feature analysis of air quality,  interpolation and prediction.
Keywords: Urban Air Computing; Machine Learning; Interpolation; Prediction; Feature Analysis