EP43B:
Machine Learning Applications in Earth Surface Processes Research I


Submit an Abstract to this Session


Session ID#: 61808

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:

Measuring the strength of earth surface process feedback from the air: new tools for quantifying dominant landscape processes and their timescales (Invited) (445521)
Laurel Larsen1, Hongxu Ma2, Christopher Tennant1 and Dino G. Bellugi3, (1)University of California Berkeley, Geography, Berkeley, CA, United States, (2)Google, Mountain View, United States, (3)University of California Berkeley, Geography, Berkeley, United States
Simulation of Fluvial Patterns Using Generative Adversarial Networks and a Database of Satellite Images (431750)
Erik Nesvold, Stanford University, Stanford, CA, United States and Tapan Mukerji, Stanford University, Department of Energy Science and Engineering; Department of Earth and Planetary Sciences; Department of Geophysics, Stanford, United States
Long-term flood hazard simulation in a coastal river reach based on least squares support vector machine regression (430917)
Maria Bermudez1,2, Luis Cea1, Jeronimo Puertas1, Javier Sopelana3 and Silda Ruano3, (1)University of A Coruña, Water and Environmental Engineering Group, A Coruña, Spain, (2)University of Granada, Environmental Fluid Dynamics Group, Granada, Spain, (3)Aquatica Ingenieria, Vigo, Spain
Object-based Correction and Mapping of LiDAR DEMs Using RTK-GPS Data and Machine Learning Modeling in the Coastal Everglades (378081)
Hannah M. Cooper, East Carolina University, Greenville, NC, United States, Caiyun Zhang, Florida Atlantic University, Boca Raton, FL, United States, Stephen E Davis III, Everglades Foundation, Palmetto Bay, FL, United States and Tiffany Troxler, Florida International University, Institute of Environment, Sea Level Solutions Center, Miami, United States
Prediction of Coastal Morphology Using Machine Learning Tools (Invited) (395245)
Nathaniel G Plant, USGS, Saint Petersburg, FL, United States
A new parameterization for wave runup within a dune impact model (398435)
Tomas Beuzen1, Evan B Goldstein2, Christopher Leaman3, Joshua Simmons1, Kilian Vos3, Kristen Splinter4, Mitchell Harley5 and Ian L Turner4, (1)Water Research Laboratory, UNSW Sydney, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (2)University of North Carolina at Greensboro, Geography, Environment, and Sustainability, Greensboro, NC, United States, (3)UNSW Sydney, Water Research Laboratory, School of Civil and Environmental Engineering, Sydney, NSW, Australia, (4)University of New South Wales, Sydney, NSW, Australia, (5)University of New South Wales, Water Research Laboratory, School of Civil and Environmental Engineering, Sydney, NSW, Australia
Deep Neural Network Classification of Littoral Systems for Change Detection (399930)
Mara Morgenstern Orescanin1, David W E Herrmann1 and Marko Orescanin2, (1)Naval Postgraduate School, Oceanography, Monterey, CA, United States, (2)California State University Monterey Bay, Seaside, CA, United States
On the use of artificial neural networks for the prediction of shoreline evolution (423062)
Giovanni Coco1, Sina Masoud-Ansari1, Jennifer Katherine Montaño Muñoz2 and Mark Gahegan3, (1)University of Auckland, School of Environment, Auckland, New Zealand, (2)Universidad Nacional, Antioquia, Medellin, Colombia, (3)University of Auckland, Center for eResearch, Auckland, New Zealand

See more of: Earth and Planetary Surface Processes