Gelman Site 1,4-Dioxane Groundwater Contamination Plume Modeling and Forecasting
dc.contributor.author | Luo, Yifan | |
dc.contributor.advisor | Gronewold, Andrew | |
dc.date.accessioned | 2022-04-20T00:42:27Z | |
dc.date.issued | 2022-04 | |
dc.date.submitted | 2022-04 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/172165 | |
dc.description.abstract | Groundwater systems are intrinsically heterogeneous with dynamic spatio-temporal patterns, which cause significant challenges in quantifying and mapping their complex processes. However, accurate forecasting of regional groundwater contamination is commonly needed to identify its spatio-temporal dynamic that helps the public anticipate the timing and severity of potential groundwater quality issues and possibly serve as an early warning system. This study focuses on modeling a plume of 1,4-dioxane originating from the Gelman site beneath the city of Ann Arbor, Michigan. It proposed a novel methodology to consider the spatially and temporally irregular and uncertain nature of groundwater contamination data to analyze the historical trends of dioxane concentration and predict its transportation: 1. A random forest interpolation model was deployed to fill in or extend fragmented time series data gaps among all the monitoring wells; 2. Mann-Kendall test was applied to evaluate the trend of dioxane concentrations at various wells; 3. An automated time series machine learning (AutoTS) package was utilized to predict the best future values forecasts; and 4. An R-based Shiny web application was designed to allow visualization and quantification of dioxane contamination analytical data. This research introduced a novel framework for filling spatial and temporal data sampling gaps in groundwater contamination to offer an effective and promising way to predict future plume concentration and spatial distribution. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | groundwater | en_US |
dc.subject | forecasting | en_US |
dc.subject | plume | en_US |
dc.title | Gelman Site 1,4-Dioxane Groundwater Contamination Plume Modeling and Forecasting | en_US |
dc.type | Practicum | en_US |
dc.description.thesisdegreename | Master of Science (MS) | en_US |
dc.description.thesisdegreediscipline | School for Environment and Sustainability | en_US |
dc.description.thesisdegreegrantor | University of Michigan | en_US |
dc.contributor.committeemember | Van Berkel, Derek | |
dc.identifier.uniqname | yifanluo | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/172165/1/Luo_Yifan_Practicum.pdf | |
dc.identifier.doi | https://dx.doi.org/10.7302/4314 | |
dc.working.doi | 10.7302/4314 | en_US |
dc.owningcollname | Dissertations and Theses (Ph.D. and Master's) |
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