Vehicle emission prediction using remote sensing data and machine learning techniques

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dc.contributor.advisor Dobbie, G en
dc.contributor.advisor Koh, YS en Chen, Jiazhen en 2018-07-17T04:27:02Z en 2018 en
dc.identifier.uri en
dc.description Full text is available to authenticated members of The University of Auckland only. en
dc.description.abstract More researchers are using remote sensing technology to measure real-world, on-road automobile emissions of nitric oxide (NO), one of the most important and frequently studied pollutants. Partnered with the National Institute of Water and Atmospheric Research (NIWA) in New Zealand, we aim to establish a robust NO emission factor prediction model using remote sensing data to forecast future emissions. We have conducted this research using real-world data that were collected over a 11-year span between 2005 and 2015. The experimental results have shown that the vehicle emission patterns are continuously changing and the relevance of remote sensing data for future predictions decays as they get older. We propose a three-step machine learning approach to establish this model. We use quantile regression forest (QRF) as the base algorithm and use random forests variable importance measure to validate and interpret the features. We have found empirically, the model is more accurate than models that are based on three other algorithms: linear regression, linear model based recursive partitioning, random forest. Lastly, we have extracted human-interpretable prediction rules from our quantile regression forest based model, using the decision based rule extraction algorithm. The rules are useful to generalise prediction logic from a black-box model such as our QRF based model. en
dc.publisher ResearchSpace@Auckland en
dc.relation.ispartof Masters Thesis - University of Auckland en
dc.relation.isreferencedby UoA99265072813802091 en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights Restricted Item. Available to authenticated members of The University of Auckland. en
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dc.rights.uri en
dc.title Vehicle emission prediction using remote sensing data and machine learning techniques en
dc.type Thesis en Computer Science en The University of Auckland en Masters en
dc.rights.holder Copyright: The author en
pubs.elements-id 747966 en Science en School of Computer Science en
pubs.record-created-at-source-date 2018-07-17 en

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