Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas.
Lakshmi Babu Saheer1, Ajay Bhasy1, Mahdi Maktabdar1
1Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom.
Frontiers in Big Data
|April 11, 2022
Summary
This study introduces an AI framework to predict urban air quality using big data, including traffic and vegetation. The model aids in developing climate change policies and sustainable urban planning.
Area of Science:
- Environmental Science
- Artificial Intelligence
- Urban Planning
Background:
- Urban air quality monitoring and prediction are crucial for climate change mitigation.
- Big data integration (pollutants, traffic, imagery, weather) offers potential for improved air quality models.
Purpose of the Study:
- To propose a novel, cost-effective AI framework for urban air quality modeling and prediction.
- To integrate diverse data sources, including aerial imagery for vegetation analysis.
Main Methods:
- Developed a comprehensive air quality modeling framework using artificial intelligence.
- Incorporated a deep learning-based vegetation detection system using aerial images.
- Evaluated various predictive models (statistical, machine learning, deep recurrent neural networks) in Cambridge, UK.
Main Results:
- The framework effectively integrates multiple data streams for air quality prediction.
- Demonstrated the utility of deep learning for vegetation analysis in urban environments.
- Identified key predictive models for urban air quality.
Conclusions:
- The proposed framework enhances urban air quality modeling capabilities.
- This approach supports sustainable urban development through informed green space and traffic planning.
- Provides evidence for developing effective climate change policies.
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