Perspective Chapter: Airborne Pollution (PM2.5) Forecasting Using Long Short-Term Memory Deep Recurrent Neural Network Optimized by Gaussian Process
Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning,...
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| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2022
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/84774 |
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| Summary: | Forecasting air pollution is a challenging problem today that requires special attention in large cities since they are home to millions of people who are at risk of respiratory diseases every day. At the same time, there has been exponential growth in the research and application of deep learning, which is useful to treat temporary data such as pollution levels, leaving aside the physical and chemical characteristics of the particles and only focusing on predicting the next levels of contamination. This work seeks to contribute to society by presenting a useful way to optimize recurrent neural networks of the short and long-term memory type through a statistical process (Gaussian processes) for the correct optimization of the processes. |
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| ISBN: | 978-1-83768-086-3 |
| DOI: | 10.5772/intechopen.108543 |