Airborne Particulate Matter Measurement And Prediction With Machine Learning Techniques

This article discusses the use of machine learning methods for predicting air pollution levels using data collected from a proprietary monitoring station. The decision tree, random forest, recurrent neural network, and long short-term memory models were tested with varying hyperparameters and time scales, with the long short-term memory algorithm showing the most optimal results. Recommendations for potential applicability of these methods are also provided.