An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics

Valentine, Andrew; Kalnins, Lara

doi:https://doi.org/10.5194/esurf-4-445-2016

Articles | Volume 4, issue 2

https://doi.org/10.5194/esurf-4-445-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue:

Frontiers in geomorphometry

https://doi.org/10.5194/esurf-4-445-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 4, issue 2

Review article

|

30 May 2016

Review article |

| 30 May 2016

An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics

Andrew Valentine and Lara Kalnins

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Final revised paper (published on 30 May 2016)
Preprint (discussion started on 02 Feb 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'On the Selection of training data', Evan Goldstein, 25 Feb 2016
RC1: 'RC', Ian Evans, 11 Mar 2016
SC2: 'It is important to "normalize" the data', Joseph Becker, 17 Mar 2016
RC2: 'introduction to learning algorithms', Niels Anders, 20 Mar 2016
EC1: 'Comments and review', John K. Hillier, 31 Mar 2016
- AC1: 'Response to all reviewer and short comments', Andrew Valentine, 28 Apr 2016

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Andrew Valentine on behalf of the Authors (28 Apr 2016) Author's response Manuscript

ED: Publish subject to technical corrections (19 May 2016) by John K. Hillier

ED: Publish subject to technical corrections (19 May 2016) by Tom Coulthard (Editor)

AR by Andrew Valentine on behalf of the Authors (19 May 2016) Manuscript

Short summary

Learning algorithms are powerful tools for understanding and working with large data sets, particularly in situations where any underlying physical models may be complex and poorly understood. Such situations are common in geomorphology. We provide an accessible overview of the various approaches that fall under the umbrella of "learning algorithms", discuss some potential applications within geomorphometry and/or geomorphology, and offer advice on practical considerations.