Book Open Access
Spatial and Spatiotemporal Interpolation / Prediction using Ensemble Machine Learning
Hengl, T.;
Parente, L.;
Bonannella, C.
This R tutorial explains step-by-step how to use Ensemble Machine Learning to generate predictions (maps) from 2D, 3D, 2D+T training (point) datasets. We show functionality to do automated benchmarking for spatial/spatiotemporal prediction problems, and for which we use primarily the mlr framework and spatial packages terra, rgdal and similar. In addition, we explain how to plot spatial/spatiotemporal prediction inputs and outputs, including how to do accuracy plots and predictograms. We focus engineering the predictive mapping around three main areas: (a) accuracy performance, (b) computing time, (c) robustness of the algorithms (sensitivity to noise, artifacts etc).
Online version of the book is available at: https://opengeohub.github.io/spatial-prediction-eml/
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Fig_general_scheme_PEM.png
md5:f7ce54a67a5e006ab868871e0fdbd469 |
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Hengl_et_al_2022_Spatial_Spatiotemporal_EML_OpenGeoHub.pdf
md5:56b3022d15662e14f2e6fad041b5da01 |
9.8 MB | Download |
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spatial-prediction-eml-master.zip
md5:5c2822b8b248471c9dc33182435211d8 |
157.2 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 337 | 314 |
| Downloads | 89 | 81 |
| Data volume | 2.8 GB | 2.6 GB |
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| Unique downloads | 66 | 60 |
- Publication date:
- January 23, 2022
- DOI:
-
- Keyword(s):
- ensemble Machine Learning predictive mapping spatial interpolation OpenLandMap
- Related identifiers:
- New versions
- 10.5281/zenodo.5513826 (Book)
- Supplementary material
- 10.5281/zenodo.5886677 (Book)
- License (for files):
- Creative Commons Attribution Share Alike 4.0 International