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

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, NOx, O-O, NH3, CO, PM(1)0, 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 (O-3) in the Region of Murcia (Spain). O-3 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 O-3 prediction models from 90% and a root mean square error less than 11 mu/mu/m(3) for the cities of the Region of Murcia involved in the study.