rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: Assessment of Supervised and Unsupervised Machine Learning Applications Toward Predicting Gulf of Mexico Coastal Hypoxia
  name:
    name part: Longridge, Samantha Claire
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: DiMarco, Steve
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Observations of dissolved oxygen, salinity, temperature, and six different nutrient concentrations of the waters on the TXLA Shelf in the months of March - September in 2003 - 2014 were used in unsupervised and supervised machine learning techniques to identify driving processes of hypoxia and examine the performance of classification algorithms on predicting hypoxia on the TXLA Shelf. Unsupervised machine learning techniques, principal component analysis, and K-means clustering, successfully identified variability patterns that were associated with previously known drivers and processes of hypoxia in the region such as vertical stratification of the water column and the Mississippi River plume. The performance of eight classification algorithms (i.e., logistic regression, LDA, QDA, na©¯ve bayes, KNN, SVM, decision tree, and random forest) on predicting hypoxia with the observations on TXLA Shelf were compared. Results showed that na©¯ve bayes performed best on classifying hypoxia with high recall and low false positive rates. Balancing the class distribution in the training set of each algorithm significantly increased performance, indicating that classifier performance was strongly dependent on input training data. This study establishes that straightforward machine learning techniques can aid in identification of known main drivers of hypoxia and their characteristics and that those characteristics can be used to predict hypoxia on the TXLA Shelf. Thees techniques have the potential to evaluate hypoxia presence or absence in hydrographic data where DO is missing and can be a powerful tool used in water quality and resource management in the region. While the approaches presented in this study were specifically for the TXLA Shelf, the methodology is applicable to other coastal systems and locations with similar datasets. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/198060
  note: by Samantha Longridge.
  note: "Major Subject: Oceanography"
  note: Includes vita.
  note: Master of Science Texas A&M University 2022 https://hdl.handle.net/1969.1/198060
  note: Includes bibliographical references.
  note: Text (Thesis)
  subject:
    topic: Major Oceanography
  subject:
    topic: Coastal Hypoxia
  subject:
    topic: Gulf of Mexico
  subject:
    topic: Machine Learning
  subject:
    topic: Supervised Machine Learning
  subject:
    topic: Unsupervised Machine Learning
  subject:
    topic: Texas-Louisiana Shelf
  subject:
    topic: Low Oxygen Conditions
  identifier: https://hdl.handle.net/1969.1/198060
  record info:
    record content source: TXA
    record creation date: 240828
    record change date: 20240829173513.2
    record identifier: in00005468528
    language of cataloging:
      language term: eng