Date of Award

2016

Document Type

Open Access Thesis

Department

Computer Science and Engineering

Sub-Department

College of Engineering and Computing

First Advisor

Csilla Farkas

Abstract

Hidden Markov Models a class of statistical models used in various disciplines for understanding speech, finding different types of genes responsible for cancer and much more. In this thesis, Hidden Markov Models are used to obtain hidden states that can correlate the flow changes in the Wakulla Spring Cave. Sensors installed in the tunnels of Wakulla Spring Cave have recorded huge correlated changes in the water flows at numerous tunnels. Assuming the correlated flow changes are a consequence of system being in a set of discrete states, a Hidden Markov Model is calculated. This model comprising all the sensors installed in these conduits can help understand the correlations among the flows at each sensor and estimate the hidden states. In this thesis, using the Baum - Welch algorithm and observations from the sensors, hidden states are calculated for the model. The observations are converted from second order to first order observations using base 3 values. The generated model can help identify the set of discrete states for the quantized flow rates at each sensor. The hidden states can predict the correlated flow changes. This document further validates the assumption of the system being in a set of discrete states

Share

COinS