Multivariate prediction of PM10 concentration by LSTM neural networks
Air presence of particulate pollutants is an environmental problem with significant health issues. Monitoring their concentration is a key factor for the correct management of urban activities. In the smart cities scenario, the most fruitful tools for such application are sensor networks combined with machine learning techniques. In this work, neural networks are employed to forecast particulate concentration of air pollutants using a novel multivariate approach.