Unsupervised Machine Learning as a Tool for Exploratory Analysis of Acoustic Telemetry Data: A Case Study With Northern Pike in Toronto Harbour
Public Deposited- Resource Type
- Creator
- Abstract
Spatial ecology aims to further knowledge of an organism's relationship with its environment and guide decision-making related to conservation. The advancement of biotelemetry has facilitated this goal, however, data management, from its acquisition to its utilization, is central to its success. Standard analysis may include separating tagged individuals into predefined groups based on biometrics or capture location, and then comparing relationships among groups, environmental measures, and their seasonal habitat choices. While effective in that it informs on the relationship among variables, this approach is computationally intensive, and the insight provided is limited to behaviour among predefined groups. This study effectively and efficiently leverages machine learning methods - hierarchical clustering and principal component analysis - to explore animal behaviour, thus providing an efficient, alternative method to analyzing acoustic telemetry data. A by-product of this project is software development that can facilitate analysis of acoustic telemetry data.
- Subject
- Language
- Publisher
- Thesis Degree Level
- Thesis Degree Name
- Thesis Degree Discipline
- Identifier
- Rights Notes
Copyright © 2021 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
- Date Created
- 2021
Relations
- In Collection:
Items
Thumbnail | Title | Date Uploaded | Visibility | Actions |
---|---|---|---|---|
agberien-unsupervisedmachinelearningasatoolforexploratory.pdf | 2023-05-05 | Public | Download |