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.
978-1-7281-5305-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/153764
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