[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121709-en":3,"doc-seo-121709-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121709,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","AN ASSESSMENT OF SUPERVISED AND UNSUPERVISED MACHINE LEARNING APPLICATIONS TOWARD PREDICTING GULF OF MEXICO COASTAL HYPOXIA - A Thesis","Observations of dissolved oxygen, salinity, temperature, and six nutrient concentrations collected on the TXLA Shelf from March to September between 2003 and 2014 support unsupervised and supervised machine-learning analyses. Principal component analysis and K-means clustering reveal variability patterns linked to known hypoxia drivers, including vertical stratification and the Mississippi River plume. Eight classification algorithms are compared for hypoxia prediction on the TXLA Shelf, and naive Bayes achieves the strongest balance of high recall with low false-positive rates. Class-balancing in training improves performance, showing dependence on input data composition.","AN ASSESSMENT OF SUPERVISED AND UNSUPERVISED MACHINE LEARNING  \nAPPLICATIONS TOWARD PREDICTING GULF OF MEXICO COASTAL HYPOXIA  \nA Thesis  \nby  \nSAMANTHA CLAIRE LONGRIDGE  \nSubmitted to the Graduate and Professional School of  \nTexas A&M University  \nin partial fulfillment of the requirements for the degree of  \nMASTER OF SCIENCE  \nChair of Committee, Committee Members,  \nHead of Department,  \nSteven F. DiMarco Jason B. Sylvan Antonietta S. Quigg  \nShari Yvon-Lewis  \nAugust 2022  \nMajor Subject: Oceanography  \nCopyright 2022 Samantha C. Longridge  \nABSTRACT  \nObservations 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, naïve bayes, KNN, SVM, decision tree, and random forest) on predicting hypoxia with the observations on TXLA Shelf were compared. Results showed that naïve 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. These 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.  \nACKNOWLEDGEMENTS  \nI would like to thank my committee chair, Dr. DiMarco and my committee members, Dr. Sylvan, and Dr. Quigg for their guidance and support throughout the course of this research. Thanks also to the friends and department faculty and staff, especially Dr. Wiederwohl and Dr. Yvon-Lewis who have mentored me and helped shape me throughout my time at Texas A&M.  \nFinally, thanks to my parents and husband for encouraging me to persevere.  \nCONTRIBUTORS AND FUNDING SOURCES  \nContributors  \nThis work was supported by a thesis committee consisting of Professor DiMarco and Professor Sylvan of the Department of Oceanography and Professor Quigg of the Department of Marine Biology.  \nThe data analyzed for Section 3 and Section 4 were provided by Professor DiMarco.  \nAll work conducted for the dissertation was completed by the student independently.  \nFunding Sources  \nGraduate study was supported by a Graduate Teaching Assistantship from Texas A&M University and two Department of Oceanography Scholarships, the Lechner Graduate Scholarship and the Luis and Elizabeth Scherck Scholarship. This work was also made possible in part by the NSF S-STEM under award \\#1355807 and the NOAA Ocean Acidification project (NA19OAR0170354) . This work was also made possible by the Society for Underwater Technology in the U.S. (SUT-US) Scholarship, the NOAA funded Mechanisms Controlling Hypoxia project (NA09N0S4780208, NA06N0S4780198, NA03N0S4780039), and the NSF Research Experience for Undergraduates under award \\# 1849932.  \nNOMENCLATURE  \nTXLA Texas-Louisiana  \nGOM ","cbCaieCMxlBTD0km","https://ap.wps.com/l/cbCaieCMxlBTD0km","pdf",26655150,1,84,"English","en",105,"# Table of Contents\n## Abstract\n## Acknowledgements\n## Contributors and Funding Sources\n## Nomenclature\n## List of Figures\n## List of Tables\n## 1. Introduction\n## 1.1 Hypoxia on the Texas-Louisiana Shelf\n## 1.2 Additional Influences of Hy","[{\"question\":\"Which data and time period are used to study Gulf of Mexico coastal hypoxia?\",\"answer\":\"The study uses observations of dissolved oxygen, salinity, temperature, and six nutrient concentrations collected on the TXLA Shelf from March–September across 2003–2014.\"},{\"question\":\"How do the unsupervised methods contribute to identifying hypoxia drivers?\",\"answer\":\"Principal component analysis and K-means clustering identify variability patterns associated with known hypoxia processes, such as vertical stratification and the Mississippi River plume.\"},{\"question\":\"Which classification algorithm performs best for hypoxia prediction and why?\",\"answer\":\"Naive Bayes performs best, delivering high recall with low false-positive rates when classifying hypoxia on the TXLA Shelf.\"},{\"question\":\"What effect does training-set class balancing have on model performance?\",\"answer\":\"Balancing the class distribution in each algorithm’s training set significantly increases performance, indicating that classifier results strongly depend on training-data composition.\"}]","AN ASSESSMENT OF SUPERVISED AND UNSUPERVISED MACHINE LEARNING APPLICATIONS TOWARD PREDICTING GULF OF MEXICO COASTAL HYPOXIA - 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