Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Paul, Varun G.
Committee Member
Mercer, Andrew E.
Committee Member
Barlow, Jeannie
Date of Degree
8-9-2022
Document Type
Graduate Thesis - Open Access
Major
Geoscience (Geology)
Degree Name
Master of Science (M.S.)
College
College of Arts and Sciences
Department
Department of Geosciences
Abstract
Groundwater-derived phosphorus has often been dismissed as a significant contributor towards surface water eutrophication, however, this dismissal is unwarranted, making the quantification of phosphorus concentrations in groundwater systems immensely important. Machine learning models have been employed to quantify the concentrations of various contaminants in groundwater, but to our best knowledge have never been used for the quantification of groundwater phosphorus. The goal of this research was to use a boosted regression tree framework to produce the first believed machine learning model of phosphorus variability in groundwater, with the High Plains aquifer serving as the study area. Results display a boosted regression tree model that was not capable of explaining and predicting the statistical variance of phosphorus throughout the aquifer under standard conditions, however important variable correlation data that can potentially be incorporated into future studies that aim to further understand phosphorus dynamics in groundwater was obtained from this research.
Recommended Citation
Temple, Jeffrey M., "Utilization of a boosted regression tree framework for prediction of dissolved phosphorus concentrations throughout the High Plains aquifer region" (2022). Theses and Dissertations. 5594.
https://scholarsjunction.msstate.edu/td/5594