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

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

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
• Public URL: https://durham-repository.worktribe.com/output/1393295