Prediction of large spatio-temporal data using machine learning methods
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Brigitte Colin Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This project was a step forward in statistical methodology for predicting green vegetation land cover in homogeneous grazing land. A supervised machine learning method, namely Boosted Regression Tree, was applied to satellite imagery. The predictive capabilities of the method was established using different data sets and approaches. Four research aims were achieved, including improved land-use prediction in a semi-arid region sensitive to climate variability.
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| ID Code: | 132263 |
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| Item Type: | QUT Thesis (PhD by Publication) |
| Supervisor: | Mengersen, Kerrie & Woodley, Alan |
| Keywords: | Boosted Regression Tree, FCover, spatio-temporal prediction, supervised machine learning, big data analysis, satellite imagery, green vegetation, data reduction and aggregation, moving window smoothing kernel, spatial neighbourhood |
| DOI: | 10.5204/thesis.eprints.132263 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 09 Sep 2019 01:22 |
| Last Modified: | 09 Sep 2019 01:22 |
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