Air presence of particulate pollutants is an environmental problem with signi¯cant 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 net- works combined with machine learning techniques. In this work, neural networks are employed to forecast particulate concentration of air pollutants using a novel multivariate approach. We analyzed ¯ve years of data relating to PM10 concentration, studying the performance of di®erent models based on the Long Short Term Memory paradigm, optimizing their hyperparameters ac- cordingly. The tests show good results in terms of approximation and generalization capabilities, along with a sensible dependence on the weather conditions.
Multivariate Prediction of PM10 Concentration by LSTM Neural Networks
Ludovico Di Antonio;Valentina Colaiuda;Annalina Lombardi;Barbara Tomassetti;
2019
Abstract
Air presence of particulate pollutants is an environmental problem with signi¯cant 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 net- works combined with machine learning techniques. In this work, neural networks are employed to forecast particulate concentration of air pollutants using a novel multivariate approach. We analyzed ¯ve years of data relating to PM10 concentration, studying the performance of di®erent models based on the Long Short Term Memory paradigm, optimizing their hyperparameters ac- cordingly. The tests show good results in terms of approximation and generalization capabilities, along with a sensible dependence on the weather conditions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
