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.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-6575 |
| Date | 09 August 2022 |
| Creators | Temple, Jeffrey M |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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