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Data analysis for predicting air pollutant concentration in Smart city Uppsala
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Pollution concentrations in urban areas are primarily from vehicular exhaust, factories, and small scale industries. Recent studies conducted by the Swedish Meteorological and Hydrological Institute (SMHI) says that 3000-5000 premature deaths [2] occur every year as a result of inhaling a high level of pollution concentrations like PM10, PM2.5, CO, Nitrogen Oxides (NO+NO2). A sustainable lifestyle in an urban city-like environment is thus possible only through smart city style urban management. Foreseeing the future, the Uppsala Municipality along with the help of IBM, Ericsson, and the Uppsala University has initiated a smart city project in Uppsala. The thrust of this initiative would be deploying pollution detection sensors all over Uppsala city and monitoring pollution concentrations continuously throughout the day. The data collected will then be passed to a knowledge discovery process that would forecast pollution concentration for the future, and will be presented in a user-friendly format in real-time using an Android application. This application will provide users with real-time pollution concentration level along with the predicted value of the location thereby helping in raising awareness of its causes and consequences. The main focus of this thesis will be in exploring the suitable data mining technique that will help in better forecasting of the pollution concentration. In addition to the data model, it also focuses on the design and implementation of an Android application targeted towards the people of Uppsala community.

Place, publisher, year, edition, pages
2016. , 72 p.
Series
Endangered languages and cultures, ISSN 1651-6540 ; 16016
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-283405OAI: oai:DiVA.org:uu-283405DiVA: diva2:919011
Educational program
Master Programme in Computer Science
Supervisors
Examiners
Available from: 2016-04-13 Created: 2016-04-12 Last updated: 2016-04-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Language
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Output format
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