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

Valentine, A. P. and Kalnins, L. M. (2016) 'An introduction to learning algorithms and potential applications in geomorphometry and earth surface dynamics.', Earth surface dynamics., 4 . pp. 445-460.

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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.5194/esurf-2016-6
Publisher statement:© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License.
Record Created:15 Mar 2016 10:35
Last Modified:08 Jun 2016 13:47

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