OD001
Accelerating Ocean Science: Using artificial intelligence and machine learning to gain new insights
Session ID#: 85899
Session Description:
For many research areas sampling technology and model capacity has advanced to the point that collecting and producing data is no longer the limiting factor when it comes to scientific pursuits. The amount of available data has advanced to the point that the main bottleneck to generating meaningful results is now the analysis of extremely large volumes of data. Up to this point most fields have used manual analysis or limited automation, methods that are quickly becoming unfeasible for huge volumes of data and complex multi-disciplinary approaches.
The advancement of accessible deep learning tools in the last few years has opened up many 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 results are the exploration of data and datasets in innovative ways. 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.
Index Terms:
1910 Data assimilation, integration and fusion [INFORMATICS]
1916 Data and information discovery [INFORMATICS]
1920 Emerging informatics technologies [INFORMATICS]
1942 Machine learning [INFORMATICS]
1942 Machine learning [INFORMATICS]
4263 Ocean predictability and prediction [OCEANOGRAPHY: GENERAL]
Primary Chair: Carrie Wall, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
Co-chairs: Jan Saynisch, GFZ - Potsdam, Potsdam, Germany, Matt Harvey, Google, LLC, San Francisco, United States and Christopher Irrgang, Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geodesy and Remote Sensing, Potsdam, Germany
Primary Liaison: Carrie Wall, University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, United States
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
A 2D Fully Convolutional Neural Network for nearshore and surf-zone bathymetry inversion from synthetic imagery of the surf-zone using the model Celeris (642325)
Adam Collins1, Katherine L Brodie2, Spicer Bak2, Ty Hesser3, Matthew Farthing3, Douglas W Gamble1 and Joseph W Long1, (1)University of North Carolina at Wilmington, Earth and Ocean Sciences, Wilmington, NC, United States, (2)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Duck, NC, United States, (3)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, United States
A Deep Learning Model to Extract Ship Size from Sentinel-1 Images (642594)
Yibin Ren1, Bin Liu2 and X. Li1, (1)CAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, China, (2)College of Marine Sciences, Shanghai Ocean University, Shanghai, China
A Study on the Prediction Algorithm of Sea Fog Dissipation based on Machine Learning (645179)
JinHyun Han1, Hyun Seok 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
Deep Learning for Detecting Gulf Stream and Eddies from Satellite Images (646482)
Deepak Subramani, Indian Institute of Science, Computational and Data Sciences, Bangalore, India, Raghav Sharma, Indian Institute of Science, Bangalore, India and Avijit Gangopadhyay, Professor of Oceanography, School for Marine Science and Technology University of Massachusetts Dartmouth 836, S. Rodney French Blvd. New Bedford, MA, New Bedford, MA, United States
Improving short-term wave forecasts with convolution neural networks and regional buoy observations (647546)
Jonny Mooneyham, Western Washington University, Computer Science, Bellingham, United States, Sean C Crosby, Western Washington University, Geology, 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, Computing & Analytics Division, Richland, WA, 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, Oceanography and Coastal Sciences, Baton Rouge, LA, 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 Choi1 and Gwang-Ho Seo2, (1)GeoSystem Research, Corp., Gunpo, South Korea, (2)Korea Hydrographic and Oceanographic Agency, Busan, South Korea
Extracting submesoscales from drifter observations using adaptive Gaussian Process regression techniques (651995)
Rafael Carvalho Gonçalves1, Mohamed Iskandarani2, George R Halliwell Jr3, Matthieu Le Henaff1 and Gustavo Jorge Goni4, (1)CIMAS/University of Miami, Miami, FL, United States, (2)University of Miami - RSMAS, Miami, FL, United States, (3)NOAA Miami, Miami, FL, United States, (4)NOAA/AOML, Miami, FL, United States
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
First Drain, Then Train: Unleash the Full Potential of AI on Large Underwater Datasets (After Removing the Water from Them) (657005)
Derya Akkaynak, Harbor Branch Oceanographic Institution, Florida Atlantic University, Ft. Pierce, United States and Tali Treibitz, University of Haifa, Charney School Of Marine Sciences, Department of Marine Technologies, Haifa, Israel
Deep learning techniques for nearshore and riverine bathymetry estimation using water-surface observations (657624)
Hojat Ghorbanidehno1, Yizhou Qian2, Jonghyun Harry Lee3, Matthew Farthing4, Ty Hesser4, Peter K Kitanidis5 and Eric F Darve1, (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, HI, United States, (4)U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, Vicksburg, MS, United States, (5)Stanford University, Department of Civil and Environmental Engineering, Stanford, CA, United States
