The Application of Machine Learning and Intelligent Sensors for Real-Time Air Quality Monitoring: A Literature Review - Mustamin, Syaiful Bachri; Atnang, Muhammad; Sahriani, Sahriani; Fajar, Nurhikmah; Sari, Sri Kurnian; Pahlawan , Muammar Reza; Amrullah, Mujahidin (Science Tech Group, 2024)
Air pollution is a global issue that has major consequences for human health and the environment. Accurate air quality prediction plays an important role in mitigating and preventing the negative impacts of air pollution. The thirteen sources analyzed in this literature study show a growing trend in the use of machine learning for air quality prediction, driven by the limitations of traditional methods and machine learning capabilities in efficiently processing complex data. This literature study examines a variety of commonly used machine learning models, such as Support Vector Regression (SVR), Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM), and evaluates their performance based on metrics such as RMSE, MAE, and R². The sources also highlight the importance of understanding the factors that affect air quality, including concentrations of various pollutants (PM2.5, PM10, NO2, CO, SO2, and ozone), meteorological data (temperature, humidity, wind speed, air pressure, precipitation, and temperature inversion), traffic data, and spatial-temporal variations. The integration of the Internet of Things (IoT) and machine learning is the main focus in the development of real-time air quality monitoring systems. IoT sensors enable the collection of real-time air quality and meteorological data, which are then processed using machine learning models to generate predictions. This literature study identifies several challenges in air quality prediction, such as data limitations, the complexity of air pollution dynamics, and ethical & privacy considerations. However, machine learning offers great potential to improve the accuracy of air quality predictions and monitoring, thus contributing to a healthier and more sustainable environment.