Articles | Volume 4, issue 2
https://doi.org/10.5194/esurf-4-445-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/esurf-4-445-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review article
| 
30 May 2016
Review article |
| 30 May 2016
An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
Department of Earth Sciences, Universiteit Utrecht, Postbus 80.021, 3508TA Utrecht, the Netherlands
Lara Kalnins
Department of Earth Sciences, Science Labs, Durham University, Durham, DH1 3LE, UK
Related subject area
Cross-cutting themes: Quantitative and statistical methods in Earth surface dynamics
Introducing standardized field methods for fracture-focused surface process research
Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering
Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites
Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application
Automated quantification of floating wood pieces in rivers from video monitoring: a new software tool and validation
Particle size dynamics in abrading pebble populations
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis
Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct
Identifying sediment transport mechanisms from grain size–shape distributions, applied to aeolian sediments
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Earth's surface mass transport derived from GRACE, evaluated by GPS, ICESat, hydrological modeling and altimetry satellite orbits
The R package “eseis” – a software toolbox for environmental seismology
Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)
A new CT scan methodology to characterize a small aggregation gravel clast contained in a soft sediment matrix
Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics
Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains
Constraining the stream power law: a novel approach combining a landscape evolution model and an inversion method
Martha Cary Eppes, Alex Rinehart, Jennifer Aldred, Samantha Berberich, Maxwell P. Dahlquist, Sarah G. Evans, Russell Keanini, Stephen E. Laubach, Faye Moser, Mehdi Morovati, Steven Porson, Monica Rasmussen, and Uri Shaanan
Earth Surf. Dynam., 12, 35–66, https://doi.org/10.5194/esurf-12-35-2024, https://doi.org/10.5194/esurf-12-35-2024, 2024
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All rocks have fractures (cracks) that can influence virtually every process acting on Earth's surface where humans live. Yet, scientists have not standardized their methods for collecting fracture data. Here we draw on past work across geo-disciplines and propose a list of baseline data for fracture-focused surface process research. We detail the rationale and methods for collecting them. We hope their wide adoption will improve future methods and knowledge of rock fracture overall.
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
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We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
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Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Hajime Naruse and Kento Nakao
Earth Surf. Dynam., 9, 1091–1109, https://doi.org/10.5194/esurf-9-1091-2021, https://doi.org/10.5194/esurf-9-1091-2021, 2021
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This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.
Hossein Ghaffarian, Pierre Lemaire, Zhang Zhi, Laure Tougne, Bruce MacVicar, and Hervé Piégay
Earth Surf. Dynam., 9, 519–537, https://doi.org/10.5194/esurf-9-519-2021, https://doi.org/10.5194/esurf-9-519-2021, 2021
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Quantifying wood fluxes in rivers would improve our understanding of the key processes in river ecology and morphology. In this work, we introduce new software for the automatic detection of wood pieces in rivers. The results show 93.5 % and 86.5 % accuracy for piece number and volume, respectively.
András A. Sipos, Gábor Domokos, and János Török
Earth Surf. Dynam., 9, 235–251, https://doi.org/10.5194/esurf-9-235-2021, https://doi.org/10.5194/esurf-9-235-2021, 2021
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Abrasion of sedimentary particles is widely associated with mutual collisions. Utilizing results of individual, geometric abrasion theory and techniques adopted in statistical physics, a new model for predicting the collective mass evolution of large numbers of particles is introduced. Our model uncovers a startling fundamental feature of collective particle dynamics: collisional abrasion may either focus size distributions or it may act in the opposite direction by dispersing the distribution.
Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
Earth Surf. Dynam., 9, 105–121, https://doi.org/10.5194/esurf-9-105-2021, https://doi.org/10.5194/esurf-9-105-2021, 2021
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Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Joel Mohren, Steven A. Binnie, Gregor M. Rink, Katharina Knödgen, Carlos Miranda, Nora Tilly, and Tibor J. Dunai
Earth Surf. Dynam., 8, 995–1020, https://doi.org/10.5194/esurf-8-995-2020, https://doi.org/10.5194/esurf-8-995-2020, 2020
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In this study, we comprehensively test a method to derive soil densities under fieldwork conditions. The method is mainly based on images taken from consumer-grade cameras. The obtained soil/sediment densities reflect
truevalues by generally > 95 %, even if a smartphone is used for imaging. All computing steps can be conducted using freeware programs. Soil density is an important variable in the analysis of terrestrial cosmogenic nuclides, for example to infer long-term soil production rates.
Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua
Earth Surf. Dynam., 8, 809–824, https://doi.org/10.5194/esurf-8-809-2020, https://doi.org/10.5194/esurf-8-809-2020, 2020
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We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
Johannes Albert van Hateren, Unze van Buuren, Sebastiaan Martinus Arens, Ronald Theodorus van Balen, and Maarten Arnoud Prins
Earth Surf. Dynam., 8, 527–553, https://doi.org/10.5194/esurf-8-527-2020, https://doi.org/10.5194/esurf-8-527-2020, 2020
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In this paper, we introduce a new technique that can be used to identify how sediments were transported to their place of deposition (transport mode). The traditional method is based on the size of sediment grains, ours on the size and the shape. A test of the method on windblown sediments indicates that it can be used to identify the transport mode with less ambiguity, and therefore it improves our ability to extract information, such as climate from the past, from sediment deposits.
Taylor Smith, Aljoscha Rheinwalt, and Bodo Bookhagen
Earth Surf. Dynam., 7, 475–489, https://doi.org/10.5194/esurf-7-475-2019, https://doi.org/10.5194/esurf-7-475-2019, 2019
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Representing the surface of the Earth on an equally spaced grid leads to errors and uncertainties in derived slope and aspect. Using synthetic data, we develop a quality metric that can be used to compare the uncertainties in different datasets. We then apply this method to a real-world lidar dataset, and find that 1 m data have larger error bounds than lower-resolution data. The highest data resolution is not always the best choice – it is important to consider the quality of the data.
Matthias Meyer, Samuel Weber, Jan Beutel, and Lothar Thiele
Earth Surf. Dynam., 7, 171–190, https://doi.org/10.5194/esurf-7-171-2019, https://doi.org/10.5194/esurf-7-171-2019, 2019
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Monitoring rock slopes for a long time helps to understand the impact of climate change on the alpine environment. Measurements of seismic signals are often affected by external influences, e.g., unwanted anthropogenic noise. In the presented work, these influences are automatically identified and removed to enable proper geoscientific analysis. The methods presented are based on machine learning and intentionally kept generic so that they can be equally applied in other (more generic) settings.
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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By using a set of evaluation methods involving GPS, ICESat, hydrological modelling and altimetry satellite orbits, we show that the novel radial basis function (RBF) processing technique can be used for processing the Gravity Recovery and Climate Experiment (GRACE) data yielding global gravity field models which fit independent reference values at the same level as commonly accepted global geopotential models based on spherical harmonics.
Michael Dietze
Earth Surf. Dynam., 6, 669–686, https://doi.org/10.5194/esurf-6-669-2018, https://doi.org/10.5194/esurf-6-669-2018, 2018
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Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of many Earth science disciplines. The overarching scope requires free integrative software that is accepted across scientific disciplines, such as R. The article introduces the R package "eseis" and illustrates its conceptual structure, available functions, and worked examples.
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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Over very long timescales, 100 000 years or more, landscapes may drastically change. Sediments preserved in these landscapes have a cosmogenic radionuclide inventory that tell us when and how fast such changes took place. In this paper, we provide first evidence of an elevated long-term erosion rate of the northwestern Campine Plateau (lowland Europe), which can be explained by the loose nature of the subsoil.
Laurent Fouinat, Pierre Sabatier, Jérôme Poulenard, Jean-Louis Reyss, Xavier Montet, and Fabien Arnaud
Earth Surf. Dynam., 5, 199–209, https://doi.org/10.5194/esurf-5-199-2017, https://doi.org/10.5194/esurf-5-199-2017, 2017
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This study focuses on the creation of a novel CT scan methodology at the crossroads between medical imagery and earth sciences. Using specific density signatures, pebbles and/or organic matter characterizing wet avalanche deposits can be quantified in lake sediments. Starting from AD 1880, we were able to identify eight periods of higher avalanche activity from sediment cores. The use of CT scans, alongside existing approaches, opens up new possibilities in a wide variety of geoscience studies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
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Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
E. Pohl, M. Knoche, R. Gloaguen, C. Andermann, and P. Krause
Earth Surf. Dynam., 3, 333–362, https://doi.org/10.5194/esurf-3-333-2015, https://doi.org/10.5194/esurf-3-333-2015, 2015
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A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs.
We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle.
This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
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.
Bischof, H., Schneider, W., and Pinz, A.: Multispectral classification of Landsat-images using neural networks, IEEE T. Geosci. Remote S., 30, 482–490, 1992.
Bishop, C.: Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
Bishop, C.: Pattern Recognition and Machine Learning, Springer, New York, USA, 2006.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001.
Brenning, A.: Spatial prediction models for landslide hazards: review, comparison and evaluation, Nat. Hazards Earth Syst. Sci., 5, 853–862, https://doi.org/10.5194/nhess-5-853-2005, 2005.
Cortes, C. and Vapnik, V.: Support-Vector Networks, Mach. Learn., 20, 273–297, 1995.
Cuadrado, D. and Perillo, G.: Principal component analysis applied to geomorpholigic evolution, Estuar. Coast. Shelf S., 44, 411–419, 1997.
Das, I., Stein, A., Kerle, N., and Dadhwal, V. K.: Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models, Geomorphology, 179, 116–125, 2012.
Dietterich, T.: Ensemble methods in machine learning, in: Multiple classifier systems, in: Lecture Notes in Computer Science, edited by: Kittler, J. and Roli, F., 1857, 1–15, Springer-Verlag, Berlin, Germany, 2000.
Dunning, S., Massey, C., and Rosser, N.: Structural and geomorphological features of landslides in the Bhutan Himalaya derived from terrestrial laser scanning, Geomorphology, 103, 17–29, 2009.
Ehsani, A. and Quiel, F.: Geomorphometric feature analysis using morphometric parameterization and artificial neural networks, Geomorphology, 99, 1–12, 2008.
Ermini, L., Catani, F., and Casagli, N.: Artificial neural networks applied to landslide susceptibility assessment, Geomorphology, 66, 327–343, 2005.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, 2006.
Friedel, M. J.: Modeling hydrologic and geomorphic hazards across post-fire landscapes using a self-organizing map approach, Environ. Modell. Soft., 26, 1660–1674, 2011.
Griffiths, G.: Stochastic Prediction in Geomorphology Using Bayesian Inference Models, Math. Geol., 14, 65–75, 1982.
Gutierrez, B. T., Plant, N. G., and Thieler, E. R.: A Bayesian network to predict coastal vulnerability to sea level rise, J. Geophys. Res.-Earth, 116, F02009, https://doi.org/10.1029/2010JF001891, 2011.
Guyon, I. and Elisseeff, A.: An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157–1182, 2003.
Hartigan, J. and Wong, M.: A K-Means Clustering Algorithm, J. Roy. Stat. Soc. C-App., 28, 100–108, 1979.
Hillier, J., Conway, S., and Sofia, G.: Perspective – Synthetic DEMs: A vital underpinning for the quantitative future of landform analysis?, Earth Surface Dynamics, 3, 587–598, 2015.
Hinton, G. and Salakhutdinov, R.: Reducing the Dimensionality of Data with Neural Networks, Science, 313, 504–507, 2006.
Hornik, K.: Approximation capabilities of multilayer feedforward networks, Neural Networks, 4, 251–257, 1991.
Jain, A.: Data clustering: 50 years beyond K-means, Pattern Recogn. Lett., 31, 651–666, 2010.
Jasiewicz, J. and Stepinski, T.: Geomorphons – a pattern recognition approach to classification and mapping of landforms, Geomorphology, 182, 147–156, 2013.
Jordan, M. and Mitchell, T.: Machine learning: Trends, perspectives, and prospects, Science, 349, 255–260, 2015.
King, R., Rowland, J., Aubrey, W., Liakata, M., Markham, M., Soldatova, L., Whelan, K., Clare, A., Young, M., Sparkes, A., Oliver, S., and Pir, P.: The robot scientist Adam, Computer, 42, 46–54, 2009.
Kohonen, T.: The Self-Organizing Map, Proceedings of the IEEE, 78, 1464–1480, 1990.
Krasnopolsky, V. and Schiller, H.: Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements, Neural Networks, 16, 321–334, 2003.
Lee, S., Choi, J., and Min, K.: Landslide susceptibility analysis and verification using the Bayesian probability model, Environ. Geol., 43, 120–131, 2002.
Li, J., Heap, A., Potter, A., and Daniell, J.: Application of machine learning methods to spatial interpolation of environmental variables, Environ. Modell. Soft., 26, 1647–1659, 2011.
Lippmann, R.: Pattern classification using neural networks, IEEE Commun. Mag., 27, 47–50, 1989.
Lloyd, S.: Least squares quantization in PCM, IEEE T. Inform. Theory, 28, 129–137, 1982.
Mackay, D.: Information Theory, Inference and Learning Algorithms, Cambridge University Press, Cambridge, UK, 2003.
Marjanović, M., Kovačević, M., Bajat, B., and Voženílek, V.: Landslide susceptibility assessment using SVM machine learning algorithm, Eng. Geol., 123, 225–234, 2011.
Markou, M. and Singh, S.: Novelty detection: a review – part 2: neural network based approaches, Signal Process., 83, 2499–2521, 2003.
Marsh, I. and Brown, C.: Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV), Appl. Acoust., 70, 1269–1276, 2009.
Marsland, S.: Novelty detection in learning systems, Neural Computing Surveys, 3, 1–39, 2002.
Martin, K., Wood, W., and Becker, J.: A global prediction of seafloor sediment porosity using machine learning, Geophys. Res. Lett., 42, 10640–10646, 2015.
Mas, J. and Flores, J.: The application of artificial neural networks to the analysis of remotely sensed data, Int. J. of Sens., 29, 617–664, 2008.
Matías, K., Ordóñez, C., Taboada, J., and Rivas, T.: Functional support vector machines and generalized linear models for glacier geomorphology analysis, Int. J. Comput. Math., 86, 275–285, 2009.
Miliaresis, G. and Kokkas, N.: Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs, Comput. Geosci., 33, 1076–1087, 2007.
Mondini, A. C., Marchesini, I., Rossi, M., Chang, K.-T., Pasquariello, G., and Guzzetti, F.: Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data, Geomorphology, 201, 135–147, 2013.
Muggleton, S.: Exceeding human limits, Nature, 440, 409–410, 2006.
Olden, J., Lawler, J., and Poff, N.: Machine Learning Methods Without Tears: A Primer for Ecologists, Q. Rev. Biol., 83, 171–193, 2008.
Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space, Philos. Mag., 2, 559–572, 1901.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Peng, L., Niu, R., Huang, B., Wu, X., Zhao, Y., and Ye, R.: Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China, Geomorphology, 204, 287–301, 2014.
Quinlan, J.: Induction of Decision Trees, Mach. Learn., 1, 81–106, 1986.
Quinlan, J.: C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, CA, USA, 1993.
Rumelhart, D., Hinton, G., and Williams, R.: Learning representations by back-propagating errors, Nature, 323, 533–536, 1986.
Sambridge, M. and Mosegaard, K.: Monte Carlo methods in geophysical inverse problems, Rev. Geophys., 40, 1–29, 2002.
Sammon, J.: A Nonlinear Mapping for Data Structure Analysis, IEEE T. Comput., C-18, 401–409, 1969.
Schaul, T., Bayer, J., Wierstra, D., Sun, Y., Felder, M., Sehnke, F., Rückstieß, T., and Schmidhuber, J.: PyBrain, J. Mach. Learn. Res., 11, 743–746, 2010.
Schmelter, M., Hooten, M., and Stevens, D.: Bayesian sediment transport model for unisize bed load, Water Resour. Res., 47, W11514, https://doi.org/10.1029/2011WR010754, 2011.
Sivia, D.: Data analysis: A Bayesian tutorial, Oxford University Press, Oxford, UK, 1996.
Smith, M., Anders, N., and Keesstra, S.: CLustre: semi-automated lineament clustering for paleo-glacial reconstruction, Earth Surf. Proc. Land., 41, 364–377, 2016.
Szalay, A. and Gray, J.: Science in an exponential world, Nature, 440, 413–414, 2006.
Tamene, L., Park, S., Dikau, R., and Vlek, P.: Analysis of factors determining sediment yield variability in the highlands of northern Ethiopia, Geomorphology, 76, 76–91, 2006.
Valentine, A., Kalnins, L., and Trampert, J.: Discovery and analysis of topographic features using learning algorithms: A seamount case-study, Geophys. Res. Lett., 40, 3048–3054, 2013.
Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Ng, A., Liu, B., Yu, P., Zhou, Z.-H., Steinbach, M., Hand, D., and Steinberg, D.: Top 10 algorithms in data mining, Knowl. Inf. Syst., 14, 1–37, 2008.
Yao, X., Tham, L., and Dai, F.: Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China, Geomorphology, 101, 572–582, 2008.
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,...
Special issue