Air Pollution Prediction Using LSTM Neural Networks
Date Issued
2019-05
Author(s)
Evkoski, Bojan
Stojanovski, Zafir
Trajkovski, Aleksandar
Gjorgjevikj, Dejan
Abstract
Air pollution in North Macedonia is 20 times over
the EU limit. Recently Skopje is mentioned as the most polluted
city in Europe. As a result, this is believed to contribute to 2000
annual premature deaths in Skopje, Tetovo and Bitola only.
Being able to forecast air pollution levels to take timely
precaution could drastically reduce these numbers. Using state
of the art recurrent neural networks known as LSTMs, we were
able to predict these levels by combining historical pollution
data and weather forecasts through meta models, achieving
mean RMSE for all sensors around 20, with the best results
having RMSE as low as 8.78, with PM10 measurements ranging
from 0 to above 1000 and are usually accompanied by a lot of
noise. In this paper we present several approaches we have tried
for solving the problem and a basic comparison between them
and we also propose a way to expand these models into a realtime system for multitarget predictions.
the EU limit. Recently Skopje is mentioned as the most polluted
city in Europe. As a result, this is believed to contribute to 2000
annual premature deaths in Skopje, Tetovo and Bitola only.
Being able to forecast air pollution levels to take timely
precaution could drastically reduce these numbers. Using state
of the art recurrent neural networks known as LSTMs, we were
able to predict these levels by combining historical pollution
data and weather forecasts through meta models, achieving
mean RMSE for all sensors around 20, with the best results
having RMSE as low as 8.78, with PM10 measurements ranging
from 0 to above 1000 and are usually accompanied by a lot of
noise. In this paper we present several approaches we have tried
for solving the problem and a basic comparison between them
and we also propose a way to expand these models into a realtime system for multitarget predictions.
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