Red tides pose a significant economic and environmental threat in the Gulf of Mexico. Detecting red tide is important for understanding this phenomenon. In this thesis, machine learning approaches based on Random Forests, Support Vector Machines and K-Nearest Neighbors have been evaluated for red tide detection from MODIS satellite images. Detection results using machine learning algorithms were compared to ship collected ground truth red tide data. This work has three major contributions. First, machine learning approaches outperformed two of the latest thresholding red tide detection algorithms based on bio-optical characterization by more than 10% in terms of F measure and more than 4% in terms of area under the ROC curve. Machine Learning approaches are effective in more locations on the West Florida Shelf. Second, the thresholds developed in recent thresholding methods were introduced as input attributes to the machine learning approaches and this strategy improved Random Forests and KNearest Neighbors approaches' F-measures. Third, voting the machine learning and thresholding methods could achieve the better performance compared with using machine learning alone, which implied a combination between machine learning models and biocharacterization thresholding methods can be used to obtain effective red tide detection results.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-4967 |
| Date | 08 June 2009 |
| Creators | Cheng, Wijian |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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