The goal of this thesis is to study and model the Diel Vertical Migration (DVM) pattern using machine learning methods. We choose an Almost Periodic Function as the mathematical model and fit the monthly averaged migration data into a 5-term Fourier series whose coefficients and frequency are functions of time. The resulting function captures the general characteristics of the DVM pattern whose period is similar yet undergoes gradual changes over time. Further correlation analyses show that the monthly averaged distribution of zooplankton and various environmental factors are strongly correlated. Therefore, we adjust the function so that the coefficients and frequency are functions of environmental factors. Besides, we also examine the pattern on finer time scales using classification algorithms. We build classifiers which predict zooplankton existence at different depths based on a set of environmental measurements. Experiments demonstrate that both of the above methods are valid in modeling the DVM pattern.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/905 |
| Date | 06 1900 |
| Creators | Zhao, Shuang |
| Contributors | Joerg Sander (Computing Science), Osmar R. Zaiane (Computing Science), Sally Leys (Biological Sciences) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 2524128 bytes, application/pdf |
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