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. We analyzed five years of data relating to PM10 concentration, studying the performance of different models based on the Long Short Term Memory paradigm, optimizing their hyperparameters accordingly. 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 / Di Antonio, L.; Rosato, A.; Colaiuda, V.; Lombardi, A.; Tomassetti, B.; Panella, M.. - (2019), pp. 423-431. (Intervento presentato al convegno 2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019 tenutosi a Xiamen, Cina) [10.1109/PIERS-Fall48861.2019.9021929].

Multivariate prediction of PM10 concentration by LSTM neural networks

Rosato A.;Panella M.
2019

Abstract

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. We analyzed five years of data relating to PM10 concentration, studying the performance of different models based on the Long Short Term Memory paradigm, optimizing their hyperparameters accordingly. The tests show good results in terms of approximation and generalization capabilities, along with a sensible dependence on the weather conditions.
2019
2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019
Multivariate prediction; environmental data; LSTM neural networks
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Multivariate prediction of PM10 concentration by LSTM neural networks / Di Antonio, L.; Rosato, A.; Colaiuda, V.; Lombardi, A.; Tomassetti, B.; Panella, M.. - (2019), pp. 423-431. (Intervento presentato al convegno 2019 Photonics and Electromagnetics Research Symposium - Fall, PIERS - Fall 2019 tenutosi a Xiamen, Cina) [10.1109/PIERS-Fall48861.2019.9021929].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1405633
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