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Publication Type : Journal Article
Publisher : ISITS International Conference on Innovative Technologies for Sustainable Earth .
Source : Volume 3 (2018)
Campus : Amritapuri
School : Computational Engineering and Networking
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Year : 2018
Abstract : Monitoring, predicting, and controlling the air quality in urban areas is one of the effective solutions for tackling the climate change problem. Leveraging the availability of big data in different domains like pollutant concentration, urban traffic, aerial imagery of terrains and vegetation, and weather conditions can aid in understanding the interactions between these factors and building a reliable air quality prediction model. This research proposes a novel cost-effective and efficient air quality modeling framework including all these factors employing state-of-the-art artificial intelligence techniques. The framework also includes a novel deep learning-based vegetation detection system using aerial images. The pilot study conducted in the UK city of Cambridge using the proposed framework investigates various predictive models ranging from statistical to machine learning and deep recurrent neural network models. This framework opens up possibilities of broadening air quality modeling and prediction to other domains like vegetation or green space planning or green traffic routing for sustainable urban cities. The research is mainly focused on extracting strong pieces of evidence which could be useful in proposing better policies around climate change.
Cite this Research Publication : A. V and Dr. Geetha Srikanth, “Mapping of Air Pollution and climatic changes using GDAS data in Palakkad Region-Kerala”, vol. 3, 2018.