Date of Award
2019
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Natural Resources Management
Department
School of Natural Resource Sciences
College
Graduate and Interdisciplinary Studies
Faculty Advisor
Simsek, Halis
Description
Blue-green algae are a major environmental concern in freshwater produce toxins and cause a wide range of problems including oxygen depletion, fish kills, harm or death to other aquatic organisms, and subsequent habitat loss. Cyanobacteria are a type of blue-green algae that form harmful algal blooms (HABs) in water ecosystems. In this study, artificial intelligence techniques, in particular artificial neural networks, were developed to estimate blue-green algae fluorescence for the year-round data collected in 2016-17 from western Lake Erie, USA. Based on the lake’s environmental conditions and available data, eight input parameters including phosphorous, nitrogen, chlorophyll-a, air temperature, water temperature, turbidity, wind speed, and pH were used to run the model. Five different learning algorithms were TESTED, and the Levenberg-Marquardt algorithm resulted in the highest R2 values of 0.98 and 0.72 for eight, and three (phosphorous, nitrogen, and chlorophyll-a) input parameters, respectively. Eight input parameters produced the best estimation approach.
Recommended Citation
Guzel, Haci Osman, "Prediction of Freshwater Harmful Algal Blooms in Western Lake Erie Using Artificial Neural Network Modeling Techniques" (2019). Natural Resources Management. 28.
https://digitalcommons.ndsu.edu/nrm/28
Rights
NDSU Policy 190.6.2
Handle Identifier
https://hdl.handle.net/10365/29605