Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM Through Satellite Image Analysis-Reference-Cited by-同舟云学术

Real-Time Spatiotemporal Air Pollution Prediction with Deep Convolutional LSTM Through Satellite Image Analysis

Published:2021 Issue: Volume: Page:315-326
ISSN:2569-7072
Container-title:Advances in Data Science and Information Engineering
language:
Short-container-title:

Author:

Muthukumar Pratyush,Cocom Emmanuel,Holm Jeanne,Comer Dawn,Lyons Anthony,Burga Irene,Hasenkopf Christa,Pourhomayoun Mohammad

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-71704-9_20

Reference17 articles.

1. With a premature death every five seconds, air pollution is violation of human rights, says un expert – united nations sustainable development. Website. https://www.un.org/sustainabledevelopment/

2. Earl Swigert. Unicef: An urban world. Website. https://www.unicef.org/sowc2012/urbanmap

3. 2018 revision of world urbanization prospects — multimedia library – united nations department of economic and social affairs. Website. https://www.un.org/development/desa/publications/2018-revision-of-world-urbanization-prospects.html.

4. N. Künzli, M. Jerrett, W.J. Mack, B. Beckerman, L. LaBree, F. Gilliland, D. Thomas, J. Peters, H.N. Hodis, Ambient air pollution and atherosclerosis in los Angeles. Environ. Health Perspect. 113(2), 201–206 (2005)

5. Y. Lin, N. Mago, Y. Gao, Y. Li, Y.-Y. Chiang, C. Shahabi, J. L. Ambite. Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, (2018), pp. 359–368.

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Using Deep Learning for Spatial and Temporal Analysis of Wildfire Start and Progression;2023 International Conference on Computational Science and Computational Intelligence (CSCI);2023-12-13

2. Predicting Atmospheric Air Pollution: A Convolutional- Transformer Approach for Spatial and Temporal Analysis of PM2.5;2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE);2023-07-24

3. Multivariate and multi-output indoor air quality prediction using bidirectional LSTM;2023 11th International Symposium on Digital Forensics and Security (ISDFS);2023-05-11

4. Multi-Pollutant Ground-level Air Pollution Prediction through Deep MeteoGCN-ConvLSTM;2022 International Conference on Computational Science and Computational Intelligence (CSCI);2022-12

5. An Efficient and Robust Deep Learning Approach to Predict Air Pollution by Employing Long Short-Term Memory;2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA);2022-09-21