An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
“Learning algorithms” are a class of computational tool designed to infer information from a data set, 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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-05-01
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| Series: | Earth Surface Dynamics |
| Online Access: | http://www.earth-surf-dynam.net/4/445/2016/esurf-4-445-2016.pdf |
| Summary: | “Learning algorithms” are a class of computational tool designed to infer
information from a data set, 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. |
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| ISSN: | 2196-6311 2196-632X |