Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean

The Southern Ocean is the largest oceanic carbon and heat sink on the planet with complex dynamics at a variety of scales. Reliable, accurate, and high resolution estimates of nitrate and carbonate system parameters (hereafter biogeochemical estimates) in the Southern Ocean would enable the analysis...

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Main Author: Nachod, Zachary
Language:unknown
Published: 2022
Subjects:
Online Access:http://hdl.handle.net/1773/48384
id ftunivwashington:oai:digital.lib.washington.edu:1773/48384
record_format openpolar
spelling ftunivwashington:oai:digital.lib.washington.edu:1773/48384 2023-05-15T18:23:52+02:00 Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean Nachod, Zachary 2022 http://hdl.handle.net/1773/48384 unknown Ocean 445; http://hdl.handle.net/1773/48384 Southern Ocean machine learning biogeochemical estimates 2022 ftunivwashington 2023-03-12T19:01:27Z The Southern Ocean is the largest oceanic carbon and heat sink on the planet with complex dynamics at a variety of scales. Reliable, accurate, and high resolution estimates of nitrate and carbonate system parameters (hereafter biogeochemical estimates) in the Southern Ocean would enable the analysis of mesoscale and submesoscale biogeochemical processes throughout the water column. This work explores the use of multiple methods, including several from the machine learning literature, for biogeochemical parameter estimation in the Southern Ocean. Training data for this work includes temperature, salinity, oxygen, and nitrate measurements from the 2019 R/V Thomas G. Thompson reoccupation of the I06S line and from Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM) floats deployed during this cruise. Four models for the estimation of nitrate were trained and validated for accuracy; these models included a random forest regression, a generalized additive model, a multiple linear regression, and a gradient boosted regression tree model. The random forest regression performed the best out of the four machine and statistical models on our nitrate test data with a median value for the absolute error of 0.09 μmol kg-1 and an interquartile range of 0.13 μmol kg-1 in the absolute error. Using this random forest model, we predicted the nitrate concentrations along the high resolution tracks of two Seagliders deployed on this cruise. We plan to later repeat this estimation process for pH along the Seaglider tracks as well. The nitrate and pH estimates from the random forest model can be used to improve our understanding of mesoscale and submesoscale processes related to carbon flux in this region of the Southern Ocean. Nachod Other/Unknown Material Southern Ocean University of Washington, Seattle: ResearchWorks Southern Ocean
institution Open Polar
collection University of Washington, Seattle: ResearchWorks
op_collection_id ftunivwashington
language unknown
topic Southern Ocean
machine learning
biogeochemical estimates
spellingShingle Southern Ocean
machine learning
biogeochemical estimates
Nachod, Zachary
Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
topic_facet Southern Ocean
machine learning
biogeochemical estimates
description The Southern Ocean is the largest oceanic carbon and heat sink on the planet with complex dynamics at a variety of scales. Reliable, accurate, and high resolution estimates of nitrate and carbonate system parameters (hereafter biogeochemical estimates) in the Southern Ocean would enable the analysis of mesoscale and submesoscale biogeochemical processes throughout the water column. This work explores the use of multiple methods, including several from the machine learning literature, for biogeochemical parameter estimation in the Southern Ocean. Training data for this work includes temperature, salinity, oxygen, and nitrate measurements from the 2019 R/V Thomas G. Thompson reoccupation of the I06S line and from Southern Ocean Carbon and Climate Observations and Modeling project (SOCCOM) floats deployed during this cruise. Four models for the estimation of nitrate were trained and validated for accuracy; these models included a random forest regression, a generalized additive model, a multiple linear regression, and a gradient boosted regression tree model. The random forest regression performed the best out of the four machine and statistical models on our nitrate test data with a median value for the absolute error of 0.09 μmol kg-1 and an interquartile range of 0.13 μmol kg-1 in the absolute error. Using this random forest model, we predicted the nitrate concentrations along the high resolution tracks of two Seagliders deployed on this cruise. We plan to later repeat this estimation process for pH along the Seaglider tracks as well. The nitrate and pH estimates from the random forest model can be used to improve our understanding of mesoscale and submesoscale processes related to carbon flux in this region of the Southern Ocean. Nachod
author Nachod, Zachary
author_facet Nachod, Zachary
author_sort Nachod, Zachary
title Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
title_short Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
title_full Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
title_fullStr Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
title_full_unstemmed Evaluation of Machine Learning Techniques for Estimating Biogeochemical Properties on Seaglider Tracks in the Southern Ocean
title_sort evaluation of machine learning techniques for estimating biogeochemical properties on seaglider tracks in the southern ocean
publishDate 2022
url http://hdl.handle.net/1773/48384
geographic Southern Ocean
geographic_facet Southern Ocean
genre Southern Ocean
genre_facet Southern Ocean
op_relation Ocean 445;
http://hdl.handle.net/1773/48384
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