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Jesus Soto

Air-Pollution Prediction in Smart Cities through Machine Learning Methods

Estamos de suerte, al final nos han aceptado el artículo que llevamos meses en revisión.

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

An unsupervised technique to discretize numerical values by fuzzy partitions


Una nueva publicación con técnicas aplicadas al análisis de datos.

An unsupervised technique to discretize numerical values by fuzzy partitions.


The numerical value discretization is a process that is performed in the data preprocessing phase of intelligent data analysis. Preprocessing phase is very relevant because the quality of the models obtained in data mining step depends on this phase. Value discretization is an important task in data preprocessing because not all data mining techniques can handle continuous values. In this paper an unsupervised technique to discretize continuous data values using fuzzy partitions is proposed. Specifically a clustering technique that gets fuzzy partitions is presented. In addition, to evaluate the behavior of the proposed technique a series of experiments have been proposed using a Extreme Learning Machine classifier and a committee of Extreme Learning Machine. Beside comparing with the K-means discretization technique. These experiments have been validated statistically obtaining the best results the approach proposed.