A machine learning approach for environmental assessment on air quality and mitigation strategy

Abstract

Air pollution has a signi0cant impact on environment resulting in consequences such as global warming and acid rain. Toxic emissions from vehicles are one of the primary sources of pollution. Assessment of air pollution data is critical in order to assist residents in locating the safest areas in the city that are ideal for life. In this work, density-based spatial clustering of applications with noise (DBSCAN) is used which is among the widely used clustering algorithms in machine learning. It is not only capable of 0nding clusters of various sizes and shapes but can also detect outliers. DBSCAN takes in two important input parameters— Epsilon (Eps) and Minimum Points (MinPts). Even the slightest of variations in the parameter values fed to DBSCAN makes a big di=erence in the clustering.,ere is a need to 0nd Eps value in as minimum time as possible. In this work, the goal is to 0nd the Eps value in less time. For this purpose, a search tree technique is used for 0nding the Eps input to the DBSCAN algorithm. Predicting air pollution is a complex task due to various challenges associated with the dynamic and multifaceted nature of the atmosphere such as meteorological variability, local emissions and sources, data quality and availability, and emerging pollutants. Extensive experiments prove that the search tree approach to 0nd Eps is quicker and e>cient in comparison to the widely used KNN algorithm.,e time reduction to 0nd Eps makes a signi0cant impact as the dataset size increases.,e input parameters are fed to DBSCAN algorithm to obtain clustering results.