A continuous variable Bayesian networks model for water quality modeling: A case study of setting nitrogen criterion for small rivers and streams in Ohio, USA. (July 2015) - Qian, Song S.; Miltner, Robert J.
Abstract: We present a continuous variable Bayesian networks modeling framework that integrates the graphical representation of a Bayesian networks model with empirical model-developing approach. Our model retains the Bayesian networks model's graphical representation of hypothesized causal connections among important variables and employs conventional statistical modeling approaches for establishing functional relationships among these variables. The modeling framework avoids discretizing continuous variables and the resulting models can be updated over time when new data are available or updated using local data to develop a site-specific model. We illustrate the modeling approach using a data for establishing nutrient criteria in streams and rivers in Ohio, U.S.A. Highlights: A continuous variable Bayesian Networks (cBN) model is developed to avoid discretization. The cBN model is a framework for combining graphical and empirical modeling approaches. A Bayesian updating process is used for localizing a model based on regional data. The modeling process is applied to the data from Ohio small rivers and streams for deriving nutrient criteria.