EP51E:
Machine Learning Applications in Earth Surface Processes Research Posters


Submit an Abstract to this Session


Session ID#: 43975

Session Description:
A rapidly-increasing amount of data is available to researchers studying earth surface processes. Complementary to these new data are the development of highly accessible machine learning tools that allow users to gain insight and make predictions with a range of techniques such as Bayesian networks, evolutionary algorithms, artificial neural networks, as well as decision and regression trees of various types. machine learning techniques can provide new and exciting ways to interpret, understand, and predict earth surface processes from small to large scales — from sediment transport and hydrodynamics to large-scale morphological behavior, ecogeomorphology, climate- and storm- driven processes, human couplings and decision support tools. We are soliciting contributions to this session that highlight the use of machine learning techniques to address issues relevant for geomorphology, earth surface processes, and coastal processes research.
Primary Convener:  Kristen Splinter, University of New South Wales, Sydney, NSW, Australia
Conveners:  Evan B Goldstein, University of North Carolina at Greensboro, Geography, Environment, and Sustainability, Greensboro, NC, United States and Margaret Palmsten, U.S. Naval Research Laboratory, Washington, DC, United States
Primary Liaison:  Kristen Splinter, University of New South Wales, Sydney, NSW, Australia
Chairs:  Evan B Goldstein, University of North Carolina at Greensboro, Geography, Environment, and Sustainability, Greensboro, NC, United States and Margaret Palmsten, U.S. Naval Research Laboratory, Washington, DC, United States
OSPA Liaison:  Evan B Goldstein, University of North Carolina at Greensboro, Geography, Environment, and Sustainability, Greensboro, NC, United States

Cross-Listed:
  • B - Biogeosciences
  • GC - Global Environmental Change
  • NH - Natural Hazards
  • OS - Ocean Sciences
Index Terms:

1824 Geomorphology: general [HYDROLOGY]
1942 Machine learning [INFORMATICS]
4217 Coastal processes [OCEANOGRAPHY: GENERAL]
4558 Sediment transport [OCEANOGRAPHY: PHYSICAL]

Abstracts Submitted to this Session:

Learning representations of salt marsh hydrodynamics (417522)
William Sheppard Kearney, University of Virginia, Department of Environmental Sciences, Charlottesville, VA, United States; Boston University, Department of Earth and Environment, Boston, MA, United States
Using “old” dogs for new tricks: Exploratory machine learning to predict submarine slope instability (393930)
Jeffrey Obelcz1, Warren T Wood2, Benjamin J Phrampus3 and Taylor Runyan Lee2, (1)US Naval Research Laboratory Stennis Space Center, Marine Geology and Geophysics, Stennis Space Center, United States, (2)US Naval Research Laboratory, Ocean Sciences Division, Washington, DC, United States, (3)US Naval Research Laboratory, Geology and Geophysics, Washington, DC, United States
Automated blur detection in UAS image datasets utilizing a machine learning approach (446286)
Tarka Wilcox1, Zachary Golden2, Boen Zhang2, Yajun An3 and Renzhi Cao4, (1)Pacific Lutheran University, Tacoma, WA, United States, (2)Pacific Lutheran University, Computer Sciences, Tacoma, United States, (3)University of Washington Tacoma Campus, Tacoma, United States, (4)Pacific Lutheran University, Computer Sciences, Tacoma, WA, United States
Soil swelling potential across Colorado: a digital soil mapping approach (451278)
Emma Stell, University of Delaware, Newark, DE, United States, Mario Guevara, University of Delaware, Plant and Soil Sciences, Newark, DE, United States and Prof. Rodrigo Vargas, Arizona State University, School of Life Sciences, Tempe, United States
Precision analysis of supervised machine learning for predicting seismic recurrence in multi-faults system (464072)
Jinsu Jang, Kangwon National University, Geophysics, Chuncheon, Korea, Republic of (South) and Byung-Dal So, Kangwon National University, Geophysics, Chuncheon, Gangwon-do, South Korea
Predicting Channel Forms From Remote Sensing Data: A Multi-tiered Machine Learning Framework (455597)
Hervé Guillon1, Colin Francis Byrne1, Belize Arela Albin Lane2, Samuel Sandoval Solis3, Gregory B Pasternack4 and Helen E Dahlke5, (1)University of California Davis, Land, Air, and Water Resources, Davis, CA, United States, (2)Utah State University, Civil and Environmental Engineering, Logan, UT, United States, (3)University of California Davis, Department of Land, Air, and Water Resources, Davis, United States, (4)University of California Davis, Department of Land, Air and Water Resources, Davis, CA, United States, (5)University of California Davis, Land, Air and Water Resources, Davis, CA, United States
A Deep Learning Framework for Vegetation Management for Electrical Utilities Using Multispectral High-Resolution Satellite Imagery (459704)
John Mbari Wamburu, University of Massachusetts Amherst, Amherst, MA, United States, Levente Klein, IBM Yorktown Heights, Yorktown Heights, United States and Hendrik Hamann, IBM Research, Yorktown Heights, United States
Advanced machine learning techniques, extensive features and simplified field investigations facilitated vegetation mapping- a case study in Dzungarian basin (363380)
Heng Zhang1, Anwar Eziz1, Jian Xiao1, Shengli Tao2, Shaopeng Wang1, Zhiyao Tang3, Jiangling Zhu1 and Jingyun Fang1, (1)Peking University, College of Urban and Environmental Sciences, Beijing, China, (2)Université Toulouse 3 Paul Sabatier, Laboratoire Evolution et Diversité Biologique (EDB), Toulouse, France, (3)Peking University, Department of Ecology, Beijing, China
High-frequency Saltmarsh Dissolved Organic Carbon Estimates via Machine Learning (463356)
Andrew M Snauffer1, Christina Codden2, Aron Stubbins3 and Amy V Mueller1, (1)Northeastern University, Departments of Civil and Environmental Engineering and Marine and Environmental Sciences, Boston, MA, United States, (2)Northeastern University, Department of Civil and Environmental Engineering, Boston, MA, United States, (3)Northeastern University, Departments of Marine and Environmental Sciences, Chemistry and Chemical Biology, and Civil and Environmental Engineering, Boston, MA, United States
A probabilistic model for sediment resuspension in oscillatory flow (455525)
Margaret Palmsten1, Allison Penko1 and Samuel P Bateman2, (1)US Naval Research Laboratory, Washington, DC, United States, (2)Naval Research Laboratory, Stennis Space Center, MS, United States
Data-driven Modelling of Sediment Concentration in an Estuary (445535)
Minsang Cho1, Heuijung SEO2 and Hyun-Doug Yoon1, (1)Myongji University, Yongin, South Korea, (2)Hyein Engineering and Construction, Seoul, South Korea
A comparison of supervised classification methods for prediction and mapping of sediment types with multibeam bathymetry and backscatter data in Buzzards Bay, Massachusetts (398252)
Zhibin Zheng, McGill University, Montreal, QC, Canada, Haoran Liu, Louisiana State University, Oceanography and Coastal Sciences, Baton Rouge, LA, United States, Jiaze Wang, University of Maryland Center for Environmental Science Horn Point Laboratory, Cambridge, LA, United States and Songjie He, Louisiana State University, Baton Rouge, United States

See more of: Earth and Planetary Surface Processes