Forecasting Method For Air Pollution Using Machine Learning

This research suggests using machine learning to predict air quality in smart cities.By analysing past data and using advanced algorithms, the model forecasts future air quality levels.Real-time sensor data is incorporated, making predictions more accurate and enabling prompt actions to reduce pollution.The study indicates that implementing this predictive system can proactively manage the environment in cities, fostering healthier living conditions and supporting sustainable urban development.Smart cities are rapidly adopting technologies to improve the quality of life for their residents, with a focus on addressing environmental challenges like air pollution.Machine learning algorithms have emerged as powerful tools for predicting air quality parameters, such as particulate matter (PM), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3).This paper presents a literature survey on the application of machine learning, specifically the random forest algorithm, in predicting air quality in smart cities.The study reviews various methodologies, data sources, feature selection techniques, and evaluation metrics used in existing research.The results and discussions highlight the effectiveness of machine learning models in accurately forecasting air quality levels, aiding in decision-making processes for pollution control and public health management in smart cities.