Date of Award
August 2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Devarshi N. Patel
Abstract
Harmful Algal Blooms (HABs) are a significant threat to aquatic ecosystems, public health, and local economies due to their production of toxins and negative impacts on water quality. This thesis investigates the correlations between cyanobacteria growth and environmental variables, including wind conditions, surface temperatures, and nutrient levels. This research uses machine learning and statistical modeling to predict HAB occurrences by analyzing these variables' historical and near real-time data. The findings indicate that wind speed, surface temperature, and nutrient levels influence cyanobacteria dynamics, with specific thresholds identified for predictive accuracy. The developed HAB predictive models and the graphical user interface (GUI) for HAB data visualization offer practical tools for early detection and management of adverse environmental and public health events. Future research will focus on refining these models by considering other environmental factors to enhance them.
Recommended Citation
Patel, Devarshi Nikhil, "Opencast: Cyanobacteria Growth And Correlations With Predictive Factors In Creation For Harmful Algal Bloom Formation" (2024). Theses and Dissertations. 6449.
https://commons.und.edu/theses/6449