Data-driven framework for understanding and predicting air quality in urban areas

Lakshmi Babu Saheer*, Ajay Bhasy, Mahdi Maktabdar, Javad Zarrin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Downloads (Pure)

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.
Original languageEnglish
Article number822573
Number of pages19
JournalFrontiers in Big Data
Volume5
Early online date25 Mar 2022
DOIs
Publication statusPublished - 25 Mar 2022
Externally publishedYes

Keywords

  • Urban air quality
  • climate change mitigation
  • urban vegetation detection
  • regression based prediction algorithms
  • machine learning and deep learning algorithms
  • aerial view image recognition
  • cost effective modeling

Cite this