Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models
Understanding ocean temperature distribution is vital for ocean stratification, currents, and marine ecosystems. This study analyzed the global 0.5-degree ocean temperature dataset from the Chinese Academy of Sciences Marine Data Center (July 2020) to identify regional temperature patterns. After st...
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/92 |
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| author | Xiaotian Ye Weifeng Zhou |
| author_facet | Xiaotian Ye Weifeng Zhou |
| author_sort | Xiaotian Ye |
| collection | DOAJ |
| description | Understanding ocean temperature distribution is vital for ocean stratification, currents, and marine ecosystems. This study analyzed the global 0.5-degree ocean temperature dataset from the Chinese Academy of Sciences Marine Data Center (July 2020) to identify regional temperature patterns. After standardizing the data, Principal Component Analysis (PCA) reduced the dimensionality from 32 to 7, preserving key temperature variations. A Gaussian Mixture Model (GMM) determined that 18 classifications were optimal by evaluating the variance and category weights. Applying GMM to the reduced data identified 18 distinct temperature distribution patterns across various marine environments, including polar currents, warm current mixing zones, ocean fronts, and enclosed basins, each with unique geographical and physical characteristics. Most classifications showed high posterior probabilities, indicating model accuracy, though lower probabilities were observed in complex regions like the Indian Ocean. The results highlight the significant roles of ocean currents, climatic phenomena, and ecological factors in temperature distribution, providing insights for ocean circulation studies, climate modeling, and marine biodiversity conservation. Future research should enhance the model accuracy by optimizing the parameters, expanding data coverage, integrating additional features, and combining marine observations with climate models to better understand ocean temperature patterns and their global climate impacts. |
| format | Article |
| id | doaj-art-668f40629de248968884795475bd4fc9 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-668f40629de248968884795475bd4fc92025-01-24T13:36:49ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-011319210.3390/jmse13010092Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture ModelsXiaotian Ye0Weifeng Zhou1East China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaEast China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, ChinaUnderstanding ocean temperature distribution is vital for ocean stratification, currents, and marine ecosystems. This study analyzed the global 0.5-degree ocean temperature dataset from the Chinese Academy of Sciences Marine Data Center (July 2020) to identify regional temperature patterns. After standardizing the data, Principal Component Analysis (PCA) reduced the dimensionality from 32 to 7, preserving key temperature variations. A Gaussian Mixture Model (GMM) determined that 18 classifications were optimal by evaluating the variance and category weights. Applying GMM to the reduced data identified 18 distinct temperature distribution patterns across various marine environments, including polar currents, warm current mixing zones, ocean fronts, and enclosed basins, each with unique geographical and physical characteristics. Most classifications showed high posterior probabilities, indicating model accuracy, though lower probabilities were observed in complex regions like the Indian Ocean. The results highlight the significant roles of ocean currents, climatic phenomena, and ecological factors in temperature distribution, providing insights for ocean circulation studies, climate modeling, and marine biodiversity conservation. Future research should enhance the model accuracy by optimizing the parameters, expanding data coverage, integrating additional features, and combining marine observations with climate models to better understand ocean temperature patterns and their global climate impacts.https://www.mdpi.com/2077-1312/13/1/92ocean temperatureGaussian Mixture Modelsthe optimal modelglobal distribution |
| spellingShingle | Xiaotian Ye Weifeng Zhou Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models Journal of Marine Science and Engineering ocean temperature Gaussian Mixture Models the optimal model global distribution |
| title | Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models |
| title_full | Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models |
| title_fullStr | Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models |
| title_full_unstemmed | Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models |
| title_short | Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models |
| title_sort | unsupervised classification of global temperature profiles based on gaussian mixture models |
| topic | ocean temperature Gaussian Mixture Models the optimal model global distribution |
| url | https://www.mdpi.com/2077-1312/13/1/92 |
| work_keys_str_mv | AT xiaotianye unsupervisedclassificationofglobaltemperatureprofilesbasedongaussianmixturemodels AT weifengzhou unsupervisedclassificationofglobaltemperatureprofilesbasedongaussianmixturemodels |