Machine learning approach to forecasting urban pollution

a case study of Quito, Ecuador

Yves Philippe Rybarczyk, Rasa Zalakeviciute

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree algorithm that learns to classify the concentrations of fine aerosols, into two categories (>15μg/m3 vs. <15μg/m3), from a limited number of parameters such as the level of precipitation and the wind speed and direction. Requiring few rules, the resulting models are able to infer the concentration outcome with significant accuracy. This fundamental research intends to be a preliminary step in the development of a web-based platform and smartphone app to alert the inhabitants of Ecuador's capital about the risk to human health, with potential future application in other urban areas
Original languageEnglish
Title of host publication2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016
PublisherIEEE
ISBN (Electronic)978-1-5090-1629-7
DOIs
Publication statusPublished - 2016
Event2016 IEEE Ecuador Technical Chapters Meeting - Guayaquil, Ecuador
Duration: 12 Oct 201614 Oct 2016

Conference

Conference2016 IEEE Ecuador Technical Chapters Meeting
Abbreviated titleETCM
CountryEcuador
CityGuayaquil
Period12/10/1614/10/16

Fingerprint

urban pollution
wind direction
particulate matter
urban area
wind velocity
aerosol
machine learning
weather condition
human health
parameter
inhabitant
city
decision

Keywords

  • machine learning
  • decision tree
  • predictive model
  • urban pollution
  • fine particulate matter

Cite this

Rybarczyk, Y. P., & Zalakeviciute, R. (2016). Machine learning approach to forecasting urban pollution: a case study of Quito, Ecuador. In 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016 [7750810] IEEE. https://doi.org/10.1109/ETCM.2016.7750810
Rybarczyk, Yves Philippe ; Zalakeviciute, Rasa. / Machine learning approach to forecasting urban pollution : a case study of Quito, Ecuador. 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016. IEEE, 2016.
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abstract = "This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree algorithm that learns to classify the concentrations of fine aerosols, into two categories (>15μg/m3 vs. <15μg/m3), from a limited number of parameters such as the level of precipitation and the wind speed and direction. Requiring few rules, the resulting models are able to infer the concentration outcome with significant accuracy. This fundamental research intends to be a preliminary step in the development of a web-based platform and smartphone app to alert the inhabitants of Ecuador's capital about the risk to human health, with potential future application in other urban areas",
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Rybarczyk, YP & Zalakeviciute, R 2016, Machine learning approach to forecasting urban pollution: a case study of Quito, Ecuador. in 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016., 7750810, IEEE, 2016 IEEE Ecuador Technical Chapters Meeting, Guayaquil, Ecuador, 12/10/16. https://doi.org/10.1109/ETCM.2016.7750810

Machine learning approach to forecasting urban pollution : a case study of Quito, Ecuador. / Rybarczyk, Yves Philippe; Zalakeviciute, Rasa.

2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016. IEEE, 2016. 7750810.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - This work addresses the question of how to predict fine particulate matter given a combination of weather conditions. A compilation of several years of meteorological data in the city of Quito, Ecuador, are used to build models using a machine learning approach. The study presents a decision tree algorithm that learns to classify the concentrations of fine aerosols, into two categories (>15μg/m3 vs. <15μg/m3), from a limited number of parameters such as the level of precipitation and the wind speed and direction. Requiring few rules, the resulting models are able to infer the concentration outcome with significant accuracy. This fundamental research intends to be a preliminary step in the development of a web-based platform and smartphone app to alert the inhabitants of Ecuador's capital about the risk to human health, with potential future application in other urban areas

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Rybarczyk YP, Zalakeviciute R. Machine learning approach to forecasting urban pollution: a case study of Quito, Ecuador. In 2016 IEEE Ecuador Technical Chapters Meeting, ETCM 2016. IEEE. 2016. 7750810 https://doi.org/10.1109/ETCM.2016.7750810