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Earth Surface Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 2
Volume 4, issue 2
Earth Surf. Dynam., 4, 445–460, 2016
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

Earth Surf. Dynam., 4, 445–460, 2016
https://doi.org/10.5194/esurf-4-445-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
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
Related subject area  
Cross-cutting themes: Quantitative and statistical methods in Earth surface dynamics
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Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Matthias Meyer, Samuel Weber, Jan Beutel, and Lothar Thiele
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Earth's surface mass transport derived from GRACE, evaluated by GPS, ICESat, hydrological modeling and altimetry satellite orbits
Christian Gruber, Sergei Rudenko, Andreas Groh, Dimitrios Ampatzidis, and Elisa Fagiolini
Earth Surf. Dynam., 6, 1203–1218, https://doi.org/10.5194/esurf-6-1203-2018,https://doi.org/10.5194/esurf-6-1203-2018, 2018
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The R package “eseis” – a software toolbox for environmental seismology
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Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)
Eric Laloy, Koen Beerten, Veerle Vanacker, Marcus Christl, Bart Rogiers, and Laurent Wouters
Earth Surf. Dynam., 5, 331–345, https://doi.org/10.5194/esurf-5-331-2017,https://doi.org/10.5194/esurf-5-331-2017, 2017
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More articles (4)
Cited articles  
Baeza, C. and Corominas, J.: Assessment of shallow landslide susceptibility by means of multivariate statistical techniques, Earth Surf. Proc. Land., 26, 1251–1263, 2001.
Bayes, T.: An essay towards solving a problem in the doctrine of chances, Philos. T. R. Soc. A, 53, 370–418, 1763.
Beechie, T. and Imaki, H.: Predicting natural channel patterns based on landscape and geomorphic controls in the Columbia River basin, USA, Water Resour. Res., 50, 39–57, 2014.
Belluco, E., Camuffo, M., Ferrari, S., Modenese, L., Silvestri, S., Marani, A., and Marani, M.: Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing, Remote Sens. Environ., 105, 54–67, 2006.
Bhattacharya, B., Price, R., and Solomatine, D.: Machine learning approach to modeling sediment transport, J.Hydraul. Eng.-ASCE, 133, 440–450, 2007.
More articles (60)
Publications Copernicus
Special issue
Frontiers in geomorphometry
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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.
Learning algorithms are powerful tools for understanding and working with large data sets,...
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Citation
Earth Surface Dynamics

An interactive open-access journal of the European Geosciences Union