Spatial and Temporal Variations on Air Quality Prediction Using Deep Learning Techniques
Author:
Affiliation:
1. 1 School of Computer Science and Engineering, Vellore Institute of Technology , Vellore , Tamil Nadu , India
Abstract
Publisher
Walter de Gruyter GmbH
Subject
General Computer Science
Link
https://www.sciendo.com/pdf/10.2478/cait-2023-0045
Reference42 articles.
1. Lin, B., J. Zhu. Changes in Urban Air Quality during Urbanization in China. – J. Clean. Prod., Vol. 188, 2018, pp. 312-321.
2. Li, L., et al. Evaluation of Future Energy Consumption on PM2.5 Emissions and Public Health Economic Loss in Beijing. – J. Clean. Prod., Vol. 187, 2018, pp. 1115-1128.
3. Li, N., et al. Potential Impacts of Electric Vehicles on Air Quality in Taiwan. – Sci. Total Environ., Vol. 566-567, 2016, pp. 919-928.
4. Wang, Y., M. Sun, X. Yang, X. Yuan. Public Awareness and Willingness to Pay for Tackling Smog Pollution in China: A Case Study. – J. Clean. Prod., Vol. 112, 2016., pp. 1627-1634
5. Kurt, A., B. Gulbagci, F. Karaca, O. Alagha. An Online Air Pollution Forecasting System Using Neural Networks. – Environ. Int., Vol. 34, 2008, No 5, pp. 592-598.
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