Chapter 2 - Application of deep learning and machine learning in air quality modeling

Abstract

Poor air quality can cause many diseases, including heart disease, stroke, chronic obstructive pulmonary disease, and lung cancer, among others. With increasing urbanization, the problems associated with air pollution become more serious. Therefore preventing the consequences ofy air pollution is an urgent problem. It is essential to study the progress of air pollution and predict air quality based on previous and current factors. Forecasting can help to know in advance the future picture, and with more detailed information and knowledge, it will be possible to apply protective measures to reduce pollution. Nowadays, two of the most powerful tools used for modeling and forecasting are machine learning and deep learning. Various methods and techniques exist in these areas, and they continue to be filled with new approaches to handle issues, such as imbalanced and noisy data, reduced computational costs, or improved prediction accuracy. Based on the characteristics of the task and the area of application, one method will be preferable. This chapter is focused on the exploration of the essential components used in air quality prediction using machine learning and deep learning techniques. The descriptions of these components and the workflow of their application are presented and discussed here in.