A.P. Valentine
An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics
Valentine, A.P.; Kalnins, L.M.
Authors
L.M. Kalnins
Abstract
"Learning algorithms" are a class of computational tool designed to infer information from a dataset, and then apply that information predictively. They are particularly well-suited to complex pattern recognition, or to situations where a mathematical relationship needs to be modelled, but where the underlying processes are not well-understood, are too expensive to compute, or where signals are over-printed by other effects. If a representative set of examples of the relationship can be constructed, a learning algorithm can assimilate its behaviour, and may then serve as an efficient, approximate computational implementation thereof. A wide range of applications in geomorphometry and earth surface dynamics may be envisaged, ranging from classification of landforms through to prediction of erosion characteristics given input forces. Here, we provide a practical overview of the various approaches that lie within this general framework, review existing uses in geomorphology and related applications, and discuss some of the factors that determine whether a learning algorithm approach is suited to any given problem.
Citation
Valentine, A., & Kalnins, L. (2016). An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics. Earth Surface Dynamics, 4, 445-460. https://doi.org/10.5194/esurf-2016-6
Journal Article Type | Article |
---|---|
Acceptance Date | May 19, 2016 |
Online Publication Date | May 30, 2016 |
Publication Date | May 30, 2016 |
Deposit Date | Feb 3, 2016 |
Publicly Available Date | Mar 16, 2016 |
Journal | Earth Surface Dynamics |
Print ISSN | 2196-6311 |
Electronic ISSN | 2196-632X |
Publisher | Copernicus Publications |
Peer Reviewed | Peer Reviewed |
Volume | 4 |
Pages | 445-460 |
DOI | https://doi.org/10.5194/esurf-2016-6 |
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Accepted Journal Article
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License.
Published Journal Article
(2.4 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
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