Air-Pollution Prediction in Smart Cities through Machine Learning Methods: A Case of Study in Murcia, Spain

Raquel Martínez-España (Universidad Catolica de Murcia, Spain)

Andrés Bueno-Crespo (Universidad Catolica de Murcia, Spain)

Isabel Timón (Universidad Catolica de Murcia, Spain)

Jesús Soto (Universidad Catolica de Murcia, Spain)

Andrés Muñoz (Universidad Catolica de Murcia, Spain)

José M. Cecilia (Universidad Catolica de Murcia, Spain)

Abstract: Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five. Smart cities are called to play a decisive role to improve such pollution by first collecting, in real-time, different parameters such as SO₂, NO_x, O₃, NH₃, CO, PM₁0, just to mention a few, and then performing the subsequent data analysis and prediction. However, some machine learning techniques may be more well-suited than others to predict pollution-like variables. In this paper several machine learning methods are analyzed to predict the ozone level (O₃) in the Region of Murcia (Spain). O₃ is one of the main hazards to health when it reaches certain levels. Indeed, having accurate air-quality prediction models is a previous step to take mitigation activities that may benefit people with respiratory disease like Asthma, Bronchitis or Pneumonia in intelligent cities. Moreover, here it is identified the most-significant variables to monitor the air-quality in cities. Our results indicate an adjustment for the proposed O₃ prediction models from 90% and a root mean square error less than 11 μ/m³ for the cities of the Region of Murcia involved in the study.

Keywords: air-pollution monitoring, hierarchical clustering, machine learning, ozone, random forest, smart cities

Categories: H.3.5, H.4, I.2