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
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- Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering D. O’Leary et al. 10.3389/fsoil.2024.1346028
- Comparing geomorphological maps made manually and by deep learning W. van der Meij et al. 10.1002/esp.5305
- Geomorphometry today I. Florinsky 10.35595/2414-9179-2021-2-27-394-448
- Mapping stony rise landforms using a novel remote sensing, geophysical, and machine learning approach S. Fraser et al. 10.1016/j.geomorph.2024.109070
- Gaussian process models—I. A framework for probabilistic continuous inverse theory A. Valentine & M. Sambridge 10.1093/gji/ggz520
- Fast imaging for the 3D density structures by machine learning approach Y. Li et al. 10.3389/feart.2022.1028399
- Chimney Identification Tool for Automated Detection of Hydrothermal Chimneys from High-Resolution Bathymetry Using Machine Learning I. Keohane & S. White 10.3390/geosciences12040176
- Developing a Technique for Automatic Lineament Identification Based on the Neural Network Approach G. Grishkov et al. 10.1134/S0001433823120101
- Identification of Artificial Levees in the Contiguous United States R. Knox et al. 10.1029/2021WR031308
- Gaussian process regression approach for predicting wave attenuation through rigid vegetation K. Ions et al. 10.1016/j.apor.2024.103935
- Magnitude Type Conversion Models for Earthquakes in Turkey and Its Vicinity with Machine Learning Algorithms K. Coban & N. Sayil 10.1080/13632469.2022.2120114
- To what extent do flood-inducing storm events change future flood hazards? M. Khanam et al. 10.5194/hess-28-3161-2024
- Frontiers in Geomorphometry and Earth Surface Dynamics: possibilities, limitations and perspectives G. Sofia et al. 10.5194/esurf-4-721-2016
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- Generating high-resolution daily soil moisture by using spatial downscaling techniques: a comparison of six machine learning algorithms Y. Liu et al. 10.1016/j.advwatres.2020.103601
- Seismic signal recognition by unsupervised machine learning W. Huang 10.1093/gji/ggz366
- A neural network for noise correlation classification P. Paitz et al. 10.1093/gji/ggx495
- Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA) H. Guillon et al. 10.1002/esp.5984
- Sediment Identification Using Machine Learning Classifiers in a Mixed-Texture Dredge Pit of Louisiana Shelf for Coastal Restoration H. Liu et al. 10.3390/w11061257
- Digital soil mapping of peatland using airborne radiometric data and supervised machine learning – Implication for the assessment of carbon stock D. O'Leary et al. 10.1016/j.geoderma.2022.116086
- Observations of intra-peatland variability using multiple spatially coincident remotely sensed data sources and machine learning D. O'Leary et al. 10.1016/j.geoderma.2023.116348
- A statistical-based reach scale classification for the lower Tapajós river channel, eastern Amazonia J. De Cortes et al. 10.1177/0309133320981550
- A Comparison of Different Regression Algorithms for Downscaling Monthly Satellite-Based Precipitation over North China W. Jing et al. 10.3390/rs8100835
- The Journal Geological Society of India: Its Journey to 100th Volume, Challenges and Way Forward G. Bhat 10.17491/jgsi/2024/173815
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Latest update: 23 Nov 2024
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