Short-term air pollution forecasting based on environmental factors and deep learning models

• Autorzy: Arsov Mirche , Zdravevski Eftim , Lameski Petre , Corizzo Roberto , Koteli Nikola , Mitreski Kosta , Trajkovik Vladimir

Identyfikatory

DOI

10.15439/2020F211

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

Słowa kluczowe

EN

recurrent neural network; atmospheric measurements; atmospheric modeling; biological system modeling; urban areas; computer architecture; predictive models

PL

rekurencyjne sieci neuronowe; pomiary atmosferyczne; modelowanie atmosfery; modelowanie systemu biologicznego; obszary miejskie; architektura komputerów; modele predykcyjne

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 15--22
• Opis fizyczny: Bibliogr. 29 poz., wykr.

Twórcy

autor

Arsov Mirche

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

autor

Zdravevski Eftim

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

autor

Lameski Petre

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

autor

Corizzo Roberto

• Department of Computer Science American University, Washington DC, USA

autor

Koteli Nikola

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

autor

Mitreski Kosta

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

autor

Trajkovik Vladimir

• Ss Cyril and Methodius University, Faculty of Computer Science and Engineering, Skopje, North Macedonia

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Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 15th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).