OD44A:
Accelerating Ocean Science: Using Artificial Intelligence and Machine Learning to Gain New Insights II Posters
Session ID#: 85899
Session Description:
For many research areas collecting and producing data no longer limits our scientific pursuits. Data volumes have increased dramatically and the main bottleneck to generate meaningful results is now the time to analyze vast datasets. Manual analysis or limited automation methods are quickly becoming unfeasible for big data and complex multi-disciplinary approaches. The recent advancement of accessible deep learning tools has opened up new possibilities for the application of artificial intelligence and machine learning models, facilitated by cloud infrastructure and scalable compute engines, to complex scientific challenges. The use of (un)supervised machine learning methods like artificial neural networks, clustering, random forests, and decision trees, in ocean sciences is an emerging and rapidly evolving field of research. The versatility of machine learning allows for novel applications and revisiting old ones such as pattern recognition, data mining and compression, spatial interpolation, parameter and error estimation, time series analysis and filtering, ocean model and forcing surrogates, observation inversion, forecasting, parameterization and many more. This session aims to examine the current practices, tools, and future direction of artificial intelligence and machine learning in processing all types of oceanographic data and invites contributions tackling key problems of ocean sciences using novel approaches from machine learning. The session is also open to common drawbacks and limitations of machine learning and how to solve them. Contributions that connect or compare machine learning to neighboring statistical or mathematical algorithms as data assimilation, MSSA, AR processes, recurrence networks or Markov chains are highly welcome too.
Co-Sponsor(s):
Primary Chair: Carrie Wall, University of Colorado at Boulder, NOAA NCEI, Boulder, United States
Co-chairs: Jan Saynisch Wagner, Helmholtz-Zentrum Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany and Christopher Irrgang, Robert Koch Institute, Climate and Societal Analytics, Center for Artificial Intelligence in Public Health Research, Berlin, Germany
Primary Liaison: Carrie Wall, University of Colorado at Boulder, NOAA NCEI, Boulder, United States
Moderators: Carrie Wall, University of Colorado at Boulder, NOAA NCEI, Boulder, United States and Jan Saynisch Wagner, Helmholtz-Zentrum Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
Student Paper Review Liaison: Carrie Wall, University of Colorado at Boulder, NOAA NCEI, Boulder, United States
Abstracts Submitted to this Session:
Target Detection and Classification Capabilities of Two Multibeam Sonars (635869)
Emma Cotter, United States, Christopher Bassett, Applied Physics Laboratory, University of Washington, Seattle, WA, United States and Brian L Polagye, University of Washington Seattle Campus, Mechanical Engineering, Seattle, WA, United States
Classifying Short-finned Pilot Whale Acoustics with Deep Learning (643772)
Virginia Pan, Duke University, Electrical and Computer Engineering, Durham, NC, United States, Prof. Doug Nowacek, PhD, Duke University, Beaufort, United States and Nicola Quick, Duke University, Durham, United States
A Study on the Prediction Algorithm of Sea Fog Dissipation based on Machine Learning (645179)
JinHyun Han1, Hyunseok Joo1, Kuk Jin Kim1, Young-Taeg Kim2 and Seok Jae Kwon2, (1)Underwater Survey Technology 21, Inc., Incheon, South Korea, (2)Korea Hydrographic and Oceanographic Agency, Busan, South Korea
Improving short-term wave forecasts with convolution neural networks and regional buoy observations (647546)
Jonny Z Mooneyham, Western Washington University, Computer Science, Bellingham, United States, Sean C Crosby, Western Washington University, Geology Department, Bellingham, WA, United States, Nirnimesh Kumar, University of Washington, Seattle, WA, United States and Brian Hutchinson, Western Washington University, Computer Science, Bellingham, WA, United States; Pacific Northwest National Laboratory, Human-Earth System & Science, Richland, United States
Sediment types identification and seafloor geomorphology classification through machine learning in Buzzards Bay, Massachusetts (648504)
Haoran Liu, Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, LA, United States, Kehui Xu, Louisiana State University, Department of Oceanography and Coastal Sciences, Baton Rouge, United States and Bin Li, Louisiana State University, Department of Experimental statistic, Baton Rouge, LA, United States
Prediction of Sea Surface Temperature off the Southern Korean Coast using Spatial-Temporal Neural Network (648839)
Youngjin CHOI1, Young-Min Park1, Young-Kwang Ju1, Seok Jae Kwon2, Young-Taeg Kim2, Heung-Bae Choi3 and Gwang-Ho Seo2, (1)GeoSystem Research, Corp., Gunpo, South Korea, (2)Korea Hydrographic and Oceanographic Agency, Busan, South Korea, (3)Geosystem Research Corporation, Marine Forecast, Gunpo, South Korea
Using machine learning to predict oceanic wind forcing errors (650471)
Christopher Irrgang1, Jan Saynisch Wagner2 and Maik Thomas1, (1)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, (2)Helmholtz-Zentrum Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany
Retrieving Global Subsurface Salinity from Satellite Observations Using Deep Learning (653075)
Lingsheng Meng, Xiamen University, College of Ocean and Earth Sciences, Xiamen, China; University of Delaware, College of Earth, Ocean, & Environment, Newark, DE, United States and Xiao-Hai Yan, University of Delaware, College of Earth, Ocean and Environment, Newark, DE, United States
A machine learning approach to simulate dissolved oxygen and summer hypoxic volume in Chesapeake Bay (655060)
Xin Yu, Virginia Institute of Marine Science, The College of William and Mary, physical sciences, Gloucester Point, VA, United States, Jian Shen, Virginia Institute of Marine Science, Gloucester Point, VA, United States and Jiabi Du, Woods Hole Oceanographic Institution, United States
Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations (657624)
Hojat Ghorbanidehno1, Yizhou Qian2, Jonghyun Harry Lee3, Matthew Farthing4, Tyler Hesser4, Peter K Kitanidis5, Eric F Darve1 and Mojtaba Forghani1, (1)Stanford University, Mechanical Engineering, Stanford, CA, United States, (2)Stanford University, Institute for Computational and Mathematical Engineering, Stanford, CA, United States, (3)University of Hawai‘i at Mānoa, Civil and Environmental Engineering, Honolulu, United States, (4)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, United States, (5)Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States
