Multi-Horizon Air Pollution Forecasting with Deep Neural Networks
Journal
Sensors
Date Issued
2021-01
Author(s)
Arsov, Mirche
Corizzo, Roberto
Koteli, Nikola
Trajkovik, Vladimir
Abstract
: Air pollution is a global problem, especially in urban areas where the population density
is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and
waste. North Macedonia, as a developing country, has a serious problem with air pollution. The
problem is highly present in its capital city, Skopje, where air pollution places it consistently within
the top 10 cities in the world during the winter months. In this work, we propose using Recurrent
Neural Network (RNN) models with long short-term memory units to predict the level of PM10
particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from
sensors placed at multiple locations in Skopje and meteorological conditions such as temperature
and humidity. We compare different deep learning models’ performance to an Auto-regressive
Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models
consistently outperform the baseline model and can be successfully employed for air pollution
prediction. Ultimately, we demonstrate that these models can help decision-makers and local
authorities better manage the air pollution consequences by taking proactive measures.
is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and
waste. North Macedonia, as a developing country, has a serious problem with air pollution. The
problem is highly present in its capital city, Skopje, where air pollution places it consistently within
the top 10 cities in the world during the winter months. In this work, we propose using Recurrent
Neural Network (RNN) models with long short-term memory units to predict the level of PM10
particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from
sensors placed at multiple locations in Skopje and meteorological conditions such as temperature
and humidity. We compare different deep learning models’ performance to an Auto-regressive
Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models
consistently outperform the baseline model and can be successfully employed for air pollution
prediction. Ultimately, we demonstrate that these models can help decision-makers and local
authorities better manage the air pollution consequences by taking proactive measures.
Subjects
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