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EVALUATING ENVIRONMENTAL VARIABLES THAT INFLUENCE POND DISSOLVED OXYGEN TO INFORM PREDICTION MODEL DEVELOPMENT
- Date Issued:
- 2022
- Abstract/Description:
- Pond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar irradiance, rainfall, pond Chl-a concentrations as well as water color images. Pearson’s correlations and stepwise regressions were used to determine the variables’ connection to DO and their potential usefulness for a prediction model. It was determined that sunlight levels play a crucial role in DO fluctuations and crashes because of its influence on pond heating, primary productivity, and pond stratification. It was also found that image data did have correlations to certain weather variables and helped improve prediction strength.
Title: | EVALUATING ENVIRONMENTAL VARIABLES THAT INFLUENCE POND DISSOLVED OXYGEN TO INFORM PREDICTION MODEL DEVELOPMENT. |
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Name(s): |
Weber, Ethan W., author Wills, Paul S. , Thesis advisor Florida Atlantic University, Degree grantor Department of Marine Science and Oceanography Charles E. Schmidt College of Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Created: | 2022 | |
Date Issued: | 2022 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 115 p. | |
Language(s): | English | |
Abstract/Description: | Pond aquaculture accounts 65% of global finfish production. A major factor limiting pond aquaculture productivity is fluctuating oxygen levels, which are heavily influenced by atmospheric conditions and primary productivity. Being able to predict DO concentrations by measuring environmental parameters would be beneficial to improving the industry’s efficiencies. The data collected included pond DO, water temperature, air temperature, atmospheric pressure, wind speed/direction, solar irradiance, rainfall, pond Chl-a concentrations as well as water color images. Pearson’s correlations and stepwise regressions were used to determine the variables’ connection to DO and their potential usefulness for a prediction model. It was determined that sunlight levels play a crucial role in DO fluctuations and crashes because of its influence on pond heating, primary productivity, and pond stratification. It was also found that image data did have correlations to certain weather variables and helped improve prediction strength. | |
Identifier: | FA00014012 (IID) | |
Degree granted: | Thesis (MS)--Florida Atlantic University, 2022. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Includes bibliography. | |
Subject(s): |
Pond aquaculture Water--Dissolved oxygen Algorithms |
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Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00014012 | |
Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |