Self-Organizing Maps to Validate Anti-Pollution Policies

  1. Herrero, Álvaro 1
  2. Tricio, Verónica 2
  3. Corchado, Emilio 3
  4. Arroyo, Ángel 1
  5. Cambra, Carlos 1
  1. 1 Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Burgos, 09006 Burgos, Spain
  2. 2 Departmento de Física, Facultad de Ciencias, Universidad de Burgos, 09002 Burgos, Spain
  3. 3 Departamento de Informática y Automática, University of Salamanca, Salamanca, Spain
Erakutsi afiliazioak +
Aldizkaria:
Logic Journal of the IGPL

ISSN: 1367-0751,  1368-9894

Argitalpen urtea: 2019

Alea: 28

Zenbakia: 4

Orrialdeak: 596-614

Mota: Artikulua

DOI: 10.1093/JIGPAL/JZZ049 GOOGLE SCHOLAR

Beste argitalpen batzuk: Logic Journal of the IGPL

Laburpena

Abstract This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently evaluate the effectiveness of the taken measures, after and during the protocol activation time.

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