Air-pollution prediction in smart city, deep learning approach
Abdellatif Bekkar, Badr Hssina, Samira Douzi, Khadija Douzi
Abstract
Abstract Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Among air pollutants, Particulate Matter with a diameter of less than $$2.5 \mu m$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2.5</mml:mn> <mml:mi>μ</mml:mi> <mml:mi>m</mml:mi> </mml:mrow> </mml:math> ( $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ) is a serious health problem. It causes various illnesses such as respiratory tract and cardiovascular diseases. Hence, it is necessary to accurately predict the $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> concentrations in order to prevent the citizens from the dangerous impact of air pollution beforehand. The variation of $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> depends on a variety of factors, such as meteorology and the concentration of other pollutants in urban areas. In this paper, we implemented a deep learning solution to predict the hourly forecast of $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> concentration in Beijing, China, based on CNN-LSTM, with a spatial-temporal feature by combining historical data of pollutants, meteorological data, and $$PM_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:msub> <mml:mi>M</mml:mi> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> concentration in the adjacent stations. We examined the difference in performances among Deep learning algorithms such as LSTM, Bi-LSTM, GRU, Bi-GRU, CNN, and a hybrid CNN-LSTM model. Experimental results indicate that our method “hybrid CNN-LSTM multivariate” enables more accurate predictions than all the listed traditional models and performs better in predictive performance.