Predictions of PM2.5 concentrations and critical events in Santiago, Chile using Recurrent Neural Networks

T. Sepulveda, B. Peralta, O. Nicolis

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferencia

Resumen

Currently, air pollution is a topic of high importance in society due to its harmful effects on human health and the environment. Among the various air pollutants, PM2.5 (particulate material with diameter less than 2.5 micrometers) is relevant because high concentrations in the air can trigger respiratory, vascular or even lung cancer problems to people that live in contamined areas. Currently, the prediction of concentration of this material in Santiago de Chile is typically based on statistical methods or classic neural networks. In this work, we propose a model for the prediction of PM2.5 concentration and its critical events in Santiago de Chile through the use of recurring LSTM and GRU networks. In particular, data from the air quality monitoring stations located in different parts of the city of Santiago is used to predict the level of pollution by hours. The work describes the experiments carried out, with particular emphasis to the preprocessing of the data for its importance in the identification of the model. The obtained experimental results show that the performance of the GRU network slightly exceeds the LSTM network, reaching a coefficient of determination greater than 0.78 in independent set of testing data, while the threshold prediction in both networks exceeds 0.87 of R2 in the same testing set. As future work, we intend extend the proposed models to a spatial prediction throughout the city of Santiago.

Idioma originalInglés
Título de la publicación alojadaIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728131856
DOI
EstadoPublicada - nov 2019
Evento2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 - Valparaiso, Chile
Duración: 13 nov 201927 nov 2019

Serie de la publicación

NombreIEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019

Conferencia

Conferencia2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019
PaísChile
CiudadValparaiso
Período13/11/1927/11/19

    Huella digital

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Ingeniería eléctrica y electrónica
  • Control y optimización
  • Redes de ordenadores y comunicaciones
  • Hardware y arquitectura
  • Sistemas de información
  • Gestión y sistemas de información
  • Ingeniería energética y tecnologías de la energía

Citar esto

Sepulveda, T., Peralta, B., & Nicolis, O. (2019). Predictions of PM2.5 concentrations and critical events in Santiago, Chile using Recurrent Neural Networks. En IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019 [8988063] (IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CHILECON47746.2019.8988063