Analytical solutions such as the Theis solution have historically been utilized to forecast changes in aquifer water levels resulting from human-driven withdrawals using pumping wells. This method, however, suffers from a number of disadvantages, such as long data acquisition times, model uncertainty, and trial-and-error calibrations. This study illustrated the effectiveness of alternate forecasting methods that utilized machine learning principles. The groundwater level dynamics of two sites located at the Virginia Eastern Shore were predicted using historical groundwater level below land surface (GWLBLS) data as the endogenous variable and local pumping data as the exogenous variable. Predicting the local pumping data from the GWLBLS values was also implemented, to not only enforce reliability of the model, but also to highlight the capability of verifying and enforcing permitted pumping data. The machine learning methods chosen for this study were the Random Forest and SARIMAX models. Historical datasets were divided into training/calibration and testing/validation sets, and the respective models were fit to the data. These calibrated models were then compared to the performance of the Theis solution. Across both study sites, the Random Forest performed best at forecasting groundwater level over time given the pumping data as an exogenous variable, with SARIMAX performing similarly to the Theis solution. The Theis solution, however, did perform well in terms of generalization ability (GA). / Master of Science / Groundwater is a vital resource for drinking, agriculture, and industry. In order to ensure aquifer health for future use, it is crucial to be able to forecast well water level in the midst of groundwater pumping. Currently, analytical solutions such as the Theis solution are utilized to predict water level over time, but data acquisition is time-consuming and many of the calibrations have to be based on trial-and-error. In this thesis, machine learning methods were explored as alternatives to the current analytical methods. The groundwater level dynamics of the two study sites, Oyster, Virginia and Temperanceville, Virginia, were used to calibrate corresponding machine learning models, called the Random Forest (RF) and SARIMAX models. While the Theis showed that it was the most adaptable model, the RF performed the best overall in terms of root mean square error and R2 scores, which were used as reliability metrics. This study provides a range of substitutes for the Theis solution that have the capability to perform better when calibrated on a site-by-site basis.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/114035 |
| Date | 09 September 2021 |
| Creators | Shirley, Kayla Celeste |
| Contributors | Environmental Science and Engineering, Widdowson, Mark A., Rippy, Megan A., Gallagher, Daniel L. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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