Abstract
Nowadays numerous urban areas have deployed a network of sensors for monitoring multiple variables of air quality. The measurements of these sensors can be treated individually—as time series—or collectively. Collectively, a variable monitored by a network of sensors can be transformed into a map embodying the same information, but converting numerical information into visual one. Once the numerical information has been transformed into maps, they can be used as images for the usual purposes of machine learning algorithms, and specially for clustering and outlier detection. Air quality is one of the main concerns in urban areas. In this work, firstly the numerical information of 12 monitoring station measuring the concentration of Ozone in Madrid (Spain) is transformed into daily maps. For this purpose a methodology for converting numerical information from a geographically distributed network of sensors into grey-scaled maps is proposed. Later, these maps are investigated for searching outliers—extreme episodes—with Density-based spatial clustering of applications with noise. Also the sensitivity of the search of extreme episodes to the methodology for transforming numerical information into maps is investigated.
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Notes
- 1.
Following the Directive 2008/50/ Royal Decree 102/2011, the minimum number of sampling points for \(O_3\) for the population of Madrid are 5 stations being at least 3 suburban. Air Quality Monitoring Network in Madrid is composed of 14 monitoring stations, with 3 suburban.
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Acknowledgment
The research leading to these results has received funding by the Spanish Ministry of Economy and Competitiveness (MINECO) for funding support through the grant FPA2016-80994-C2-1-R, and “Unidad de Excelencia María de Maeztu”: CIEMAT - FÍSICA DE PARTÍCULAS through the grant MDM-2015-0509.
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Cárdenas-Montes, M. (2020). Search of Extreme Episodes in Urban Ozone Maps. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_16
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