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Regularized Regression Methods and Neural Networks for Modeling Fish Population Health with Water Quality Variables in the Athabasca Oil Sands Region

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dc.contributor.advisor Deeth, Lorna
dc.contributor.advisor Feng, Zeny
dc.contributor.author McMillan, Patrick
dc.date.accessioned 2021-05-11T18:32:09Z
dc.date.available 2021-05-11T18:32:09Z
dc.date.copyright 2021-04
dc.date.created 2021-04-20
dc.identifier.uri https://hdl.handle.net/10214/25704
dc.description.abstract This thesis aims to develop statistical models for fish population health measures including adjusted trout-perch body weight, gonad weight, and liver weight with the use of climate, environmental, and water quality variables measured in the Athabasca River. To identify relevant variables, we considered three variable selection techniques: stepwise regression, the lasso, and the elastic net (EN). The lasso and EN generally produced regression models with better performance for each response. Uranium (U), tungsten, tellurium (Te), pH, molybdenum (Mo), and antimony were found important for at least one response. Uranium, Te, and Mo had relatively large coefficients in both the adjusted gonad and liver weight models suggesting they may be influential on the development of trout-perch organs. Neural networks (NNs) are considered to improve the prediction accuracy of the fish population endpoints. The NNs were found to outperform the regularization techniques in predicting the adjusted body weight, but not the adjusted gonad or liver weights. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Neural Network en_US
dc.subject Variable Selection en_US
dc.subject Bayesian Hyperparameter Optimization en_US
dc.subject Sentinel Fish Populations en_US
dc.subject Oil Sands en_US
dc.subject Environmental Monitoring en_US
dc.title Regularized Regression Methods and Neural Networks for Modeling Fish Population Health with Water Quality Variables in the Athabasca Oil Sands Region en_US
dc.type Thesis en_US
dc.degree.programme Mathematics and Statistics en_US
dc.degree.name Master of Science en_US
dc.degree.department Department of Mathematics and Statistics en_US
dc.degree.grantor University of Guelph en_US


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Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International
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