UPDF AI

Aim in Climate Change and City Pollution

P. Torres,B. Sirmaçek,S. Hoyas,Ricardo Vinuesa

2021 · DOI: 10.1007/978-3-030-58080-3_290-1
Artificial Intelligence in Medicine · 0 Citations

TLDR

In this chapter, machine-learning methods have proved to importantly increase the accuracy of traditional air-pollution approaches while limiting the development cost of the models.

Abstract

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.