Air Quality Management in Smart Cities by leveraging Machine Learning Techniques

In today’s world Machine Learning (ML) algorithms are actively employed to solve several real-world problems. It is used further to forecast the Air Quality Index (AQI) in smart cities, utilizing data from sensors to predict pollution levels and provide real-time information to the public. ML models are trained on well-processed datasets, and their effectiveness is assessed using standard evaluation metrics. The predictions generated by these ML models play a crucial role in air quality management strategies, such as issuing early warnings for days with poor air quality, informing policy decisions to mitigate environmental pollution, and guiding urban planning initiatives. This research underscores the importance and application of ML in predicting air quality within smart cities.