|A Study of Nutrient Dynamics in Old Woman Creek Using Artificial Neural Networks and Bayesian Belief Networks|
|Master of Science (MS), Ohio University, 2009, Industrial and Systems Engineering (Engineering and Technology)|
The Old Woman Creek National Estuary is studied in this project to evaluate effective modelling techniques for predicting Net Ecosystem Metabolism (NEM). NEM is modelled using artificial neural networks, Bayesian belief networks, and a hybrid model. A variety of data preprocessing techniques are considered prior to model development. The effects of discretization on model development are considered and discrete data is ultimately used to produce models which classify NEM into three ranges based on inputs with information significance. Artificial neural networks are found to be the most accurate for classification while Bayesian belief networks are found to provide a better framework for dynamically predicting NEM as inputs are changed.
Committee: Gary R. Weckman, PhD (Advisor); David Millie, PhD (Committee Member); Kevin Berisso, PhD (Committee Member); Diana Schwerha, PhD (Committee Member)
Ecology; Engineering; Environmental Engineering; Industrial Engineering
Keywords: BBN; ANN; ecology; NEM; Bayesian Belief Networks; Artificial Neural Networks; computer modelling