Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/24628
Author(s): | Mariano, P. Almeida, S. M. Santana, P. |
Editor: | Solic, P., Nizetic, S., Rodrigues, J. J. P. C., Lopez-de-Ipina Gonzalez-de-Artaza, D., Perkovic, T., Catarinucci, L., and Patrono, L. |
Date: | 2020 |
Title: | Pollution prediction model using data collected by a mobile sensor network |
Event title: | 5th International Conference on Smart and Sustainable Technologies, SpliTech 2020 |
ISBN: | 978-953-290-105-4 |
DOI (Digital Object Identifier): | 10.23919/SpliTech49282.2020.9243844 |
Keywords: | Machine learning Air pollution Geographic information system |
Abstract: | In this paper we investigate how to build a model to predict pollution levels using geographical information. By focusing on this kind of attributes we hope to contribute to an effective city management as we will find the urban configurations that conduct to the lowest pollution levels. We used decision trees to build a regression model. We performed a parameter grid search using cross validation. Ablation analysis where some attributes were removed from training showed that geographical based attributes impact the prediction error of decision trees. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Description | Size | Format | |
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conferenceobject_77515.pdf | Versão Aceite | 516,24 kB | Adobe PDF | View/Open |
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