Theses and Dissertations


Nate Phillips

Issuing Body

Mississippi State University


Swan II, Edward J.

Committee Member

Zhang, Song

Committee Member

McAnally, William H.

Date of Degree


Document Type

Graduate Thesis - Open Access

Degree Name

Master of Science


James Worth Bagley College of Engineering


Data generated by complex, computational models can provide highly accurate predictions of hydrological and hydrodynamic data in multiple dimensions. Unfortunately, however, for large data sets, running these models is often timeconsuming and computationally expensive. Thus, finding a way to reduce the running time of these models, while still producing comparable results, is of notable interest. The Inference Engine is a proposed system for doing just this. It takes previously generated model data and uses them to predict additional data. Its performance, both accuracy and running time, has been compared to the performance of the actual models, in increasingly difficult data prediction tasks, and it is able, with sufficient accuracy, to quickly predict unknown model data.