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

Valentine, A.P.; Kalnins, L.M.

doi:10.5194/esurf-2016-6

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

Valentine, A.P.; Kalnins, L.M.

Authors

A.P. Valentine

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