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Permanent link (DOI): https://doi.org/10.7939/R3R92J

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Implementation and Evaluation of Spatiotemporal Prediction Algorithms and Prediction of Spatially Distributed Greenhouse Gas Inventories Open Access

Descriptions

Other title
Subject/Keyword
hierarchical
inventories
multiresolution
spatial
greenhouse gas
emissions
prediction
spatiotemporal
estimation
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Rodway, James EA
Supervisor and department
Musilek, Petr (Electrical and Computer Engineering)
Examining committee member and department
Musilek, Petr (Electrical and Computer Engineering)
Szymanski, Jozef (Civil and Environmental Engineering)
Reformat, Marek (Electrical and Computer Engineering)
Department
Department of Electrical and Computer Engineering
Specialization

Date accepted
2011-09-15T20:43:13Z
Graduation date
2011-11
Degree
Master of Science
Degree level
Master's
Abstract
Growing environmental concerns require monitoring and modelling of greenhouse gases. These modelling efforts require processing of massive datasets in a timely fashion. This, in turn, can lead to feasibility problems when estimating values of missing data points. This thesis examines and compares multiple methods for estimating values of missing data points, including their spatiotemporal extensions. Resulting predictions are compared from the perspective of accuracy and computational efficiency. The results show that kriging based methods generally outperform the others in terms of accuracy, but took longer to process. Hierarchical methods prove to be a more suitable choice, providing slightly less accurate results at much shorter times, especially for dense datasets. The second part of the thesis explores a scheme for updating emission inventories using socioeconomic data. Random forest and extreme machine learning techniques applied for this task show poor performance on real-world data.
Language
English
DOI
doi:10.7939/R3R92J
Rights
License granted by James Rodway (jrodway@ualberta.ca) on 2011-09-09T21:58:03Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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