Aim in Climate Change and City Pollution
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Abstract (arXiv)
The sustainability of urban environments is an increasingly relevant problem. Air pollution plays a key role in the degradation of the environment as well as the health of the citizens exposed to it. In this chapter we provide a review of the methods available to model air pollution, focusing on the application of machine-learning methods. In fact, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models. Machine-learning tools have opened new approaches to study air pollution, such as flow-dynamics modelling or remote-sensing methodologies.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 30 Dec 2021.
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References · 3
- 2019The role of artificial intelligence in achieving the Sustainable Development Goalscited by 100
- 2020Convolutional-network models to predict wall-bounded turbulence from wall quantitiescited by 47
- 2019Prediction of wall-bounded turbulence from wall quantities using convolutional neural networkscited by 4
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