Prediction of Air Pollution Based on Traffic and Weather Data Using Artificial Intelligence

Abstract: Criteria airborne pollutants, such as particulate matter (PM2.5) and sulfur dioxide (SO2), can cause harm to human health and the environment. Underserved areas near major interstates and industry, such as Prichard, Alabama, can easily be affected by increased air pollution with no government action to help alleviate the situation. This project focuses on developing an artificial neural network (ANN) that makes accurate air pollution predictions based on historical traffic data from surrounding highways, meteorological data, and resultant air pollution. Provided enough information, the neural network will recognize underlying patterns and trends within the weather, traffic, and pollution data. Therefore, when provided with new inputs, the neural network is able to produce an output that serves as a good approximation based on previously learned information. For a neural network to produce accurate results it is important to also take into account all environmental factors that can affect the output variable, for example, air temperature, air humidity, wind speeds, and precipitation. This research will help determine the factors that contribute the most to air quality in Prichard, while also creating a predictive model for air pollution based on future traffic patterns in the area.

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