Air Pollution Monitoring and Prediction during Metro Construction using Machine Learning

A rapid urbanization and infrastructural developments in Chennai have brought forth numerous challenges, with air pollution being a significant concern during metro construction. This project aims to address the issue of air pollution during metro construction using a data-driven machine learning approach. By leveraging historical environmental data, construction activities, and meteorological factors, seeking to predict air pollution levels and implement proactive measures to minimize its impact on public health and the environment. Various machine learning algorithms, including regression models, time series analysis, and ensemble methods, are implemented to predict air pollution levels based on historical patterns and real-time inputs. Furthermore, clustering algorithms are employed to identify spatial patterns of pollution and potential hotspots during construction. The experimental results highlight the impressive predictive accuracy of the machine-learning approach, offering a robust framework for anticipating air pollution levels during metro construction. Integrating regression models, time series analysis, ensemble methods, and spatial clustering algorithms, the methodology provides a comprehensive and effective solution for pollution mitigation in urban development.