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Air Pollution Forecasting using Machine Learning and Deep Learning Techniques

. Samriddhi Banara, Teena Singh, Yash Thenuia, Himanshu Nandanwar and Anamika Chauhan


Industrialization, urbanization, and human activity have combined to lead to air pollution, which is a major health concern for many nations around the world. When it comes to air pollution, PM2.5, defined as particles having a diameter of less than 2.5 mm, poses a major health danger. It causes a variety of symptoms, including respiratory and cardiovascular problems. The ability to predict air pollution PM2.5 levels is therefore vital for preventing the harmful effects of air pollution. This research is being carried out for two reasons. This first objective is to identify potential sources of air pollution. As well as incorporating meteorological factors, transportation considerations shall be taken into account. Lastly, we intend to select the best model      to predict air pollutant concentrations. To estimate hourly air pollution concentrations, we use machine learning and deep learning models on the acquired data.

Index Terms—air pollution, machine learning, deep learning

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