A short‐term deep learning model for urban pollution forecasting with incomplete data

Abstract

AbstractA deep neural network model for the short term prediction of ozone, 10 μm particulate matter, and 2.5 μm particulate matter concentrations in a major northwestern metropolitan area of México is developed. In order to formulate such a model, the data available from the local air quality automatic network monitoring system are used for training, validation, and testing purposes. Such time series data are incomplete and a procedure of missing data imputation is carried out. The model predicts with high accuracy the concentration of the target pollutants, and the training procedure, performance metrics, and tools used are discussed in this work. Such a model can be deployed for the implementation and evaluation of public politics for improving population health, and reducing the potential negative impacts of harmful pollutants by issuing early warnings on dangerous pollution levels.