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

Valentine, Anne; Kalnins, L. M.

doi:10.5194/esurf-4-445-2016

Cited by 37 publications

(18 citation statements)

References 61 publications

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“…Realistic synthetic DEMs offer a way to assess and understand geomorphological data, allowing users to proceed with uncertaintyaware landscape analysis to examine physical processes. Valentine and Kalnins (2016) offer an overview about machine learning and its potential in geosciences. Learning algorithms come from the computer science world, and they are designed to replicate the human approach of inferring information from a dataset, and then apply that information predictively.…”

Section: Perspectivesmentioning

confidence: 99%

Frontiers in Geomorphometry and Earth Surface Dynamics: Possibilities, Limitations and Perspectives

Sofia¹,

Hillier²,

Conway³

2016

Preprint

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Abstract. Geomorphometry, the science of quantitative land-surface analysis, has become a flourishing interdisciplinary subject, with applications in numerous fields. The interdisciplinarity of geomorphometry is its greatest strength and also one of its major challenges. Gaps are still present between the process focussed fields (e.g. soil science, glaciology, volcanology) and the technical domain (such as computer science, statistics…) where approaches and theories are developed. Thus, interesting geomorphometric applications struggle to jump between process-specific disciplines, but also struggle to take advantage of advances in computer science and technology. This special issue is therefore focused on facilitating cross-fertilization between disciplines, and highlighting novel technical developments and innovative applications of geomorphometry to various Earth-surface processes. The issue collects a variety of contributions which fall into two main categories: Perspectives and Research, further divided into "Research and innovation techniques" and "Research and innovative applications". It showcases potentially exciting developments and tools which are the building blocks for the next step-change in the field.

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Section: Perspectivesmentioning

confidence: 99%

Frontiers in Geomorphometry and Earth Surface Dynamics: Possibilities, Limitations and Perspectives

Sofia¹,

Hillier²,

Conway³

2016

Preprint

View full text Add to dashboard Cite

show abstract

“…Realistic synthetic DEMs offer a way to assess and understand geomorphological data, allowing users to proceed with uncertainty-aware landscape analysis to examine physical processes. Valentine and Kalnins (2016) offer an overview about machine learning and its potential in geosciences. Learning algorithms come from the computer science world, and they are designed to replicate the human approach of inferring information from a data set, and then apply that information predictively.…”

Section: Perspectivesmentioning

confidence: 99%

Frontiers in Geomorphometry and Earth Surface Dynamics: possibilities, limitations and perspectives

2016

View full text Add to dashboard Cite

Abstract. Geomorphometry, the science of quantitative land-surface analysis, has become a flourishing interdisciplinary subject, with applications in numerous fields. The interdisciplinarity of geomorphometry is its greatest strength and also one of its major challenges. Gaps are still present between the process focussed fields (e.g. soil science, glaciology, volcanology) and the technical domain (such as computer science, statistics . . . ) where approaches and theories are developed. Thus, interesting geomorphometric applications struggle to jump between process-specific disciplines, but also struggle to take advantage of advances in computer science and technology. This special issue is therefore focused on facilitating cross-fertilization between disciplines, and highlighting novel technical developments and innovative applications of geomorphometry to various Earthsurface processes. The issue collects a variety of contributions which fall into two main categories: Perspectives and Research, further divided into "Research and innovative techniques" and "Research and innovative applications". It showcases potentially exciting developments and tools which are the building blocks for the next step-change in the field.

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“…The slope, aspect, curvature, and relative position of features and terrain variability were the derived secondary features of the bathymetry and backscatter datasets [36,37]. Some previous studies suggested a range of secondary features that were possibly associated with substrate types from terrain analysis, which includes the bathymetric position index (BPI), roughness, curvature, aspect, Moran's I, and Sobel filter [28][29][30]. BPI is the vertical difference between a cell and the mean of the local neighborhood [36], which is deemed to be significant for sediment transport under the effect of waves and currents.…”

Section: Primary and Secondary Datamentioning

confidence: 99%

Sediment Identification Using Machine Learning Classifiers in a Mixed-Texture Dredge Pit of Louisiana Shelf for Coastal Restoration

Liu

et al. 2019

Water

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Machine learning classifiers have been rarely used for the identification of seafloor sediment types in the rapidly changing dredge pits for coastal restoration. Our study uses multiple machine learning classifiers to identify the sediment types of the Caminada dredge pit in the eastern part of the submarine sandy Ship Shoal of the Louisiana inner shelf of the United States (USA), and compares the performance of multiple supervised classification methods. High-resolution bathymetry and backscatter data, as well as 58 sediment grab samples were collected in the Caminada pit in August 2018, about two years after dredging. Two primary features (bathymetry and backscatter) and four secondary features were selected in the machine learning models. Three supervised classifications were tested in the study area: Decision Trees, Random Forest, and Regularized Logistic Regression. The models were trained using three different combinations of features: (1) all six features, (2) only bathymetry and backscatter features, and (3) a subset of selected features. The best performing model was the Random Forest method, but its performance was relatively poor when dealing with a few mixed (sand and mud) surficial sediment samples. The model provides a new and efficient method to predict the change of sediment distribution inside the Caminada pit over time, and is more reliable when predicting mixed bed with rough pit bottoms. Our results can be used to better understand the impacts on biological communities by (1) direct defaunation after initial sand excavation, (2) later mud accumulation in topographic lows, and (3) other geological and physical processes. In the future, the deposition and redistribution of mud inside the Caminada pit will continue, likely impacting benthos and water quality. Backscatter, roughness derived from bathymetry, rugosity derived from backscatter, and bathymetry (in the importance order from high to low) were identified as the most effective predictors of sediment texture for mineral resources management.

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

Cited by 37 publications

References 61 publications

Frontiers in Geomorphometry and Earth Surface Dynamics: Possibilities, Limitations and Perspectives

Frontiers in Geomorphometry and Earth Surface Dynamics: Possibilities, Limitations and Perspectives

Frontiers in Geomorphometry and Earth Surface Dynamics: possibilities, limitations and perspectives

Sediment Identification Using Machine Learning Classifiers in a Mixed-Texture Dredge Pit of Louisiana Shelf for Coastal Restoration

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