Cornell team develops computationally efficient machine learning models for hyperlocal models of PM2.5 concentrations

Cornell engineers have developed machine learning models to simplify and reinforce models to calculate the fine particulate matter (PM2.5) contained in urban air pollution. Described in a paper in the journal Transportation Research Part D: Transport and Environment, the modeling approach has low data requirements and is computationally efficient. Previous...

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New York ,United States ,Oliver Gao ,Cornell Atkinson ,College Of Engineering ,Convolutional Neural Network ,Transportation Research Part ,Howard Simpson Professor ,Environmental Engineering ,New York City ,Convolutional Long Short Term Memory ,Mean Relative Error ,University Transportation Centers Program ,