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" Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling "
Amirul Islam Rajib
Chang, Shoou-Yuh
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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803998
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| Doc. No
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TL48810
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| Call number
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1803936543; 10118466
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| Main Entry
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Islam, Mohammad Moshfiqul
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| Title & Author
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Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling\ Amirul Islam RajibChang, Shoou-Yuh
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| College
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North Carolina Agricultural and Technical State University
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| Date
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2016
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| Degree
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M.S.
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| field of study
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Civil Engineering
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| student score
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2016
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| Page No
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76
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| Note
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Committee members: Jha, Manoj K.; Teasley, Stephanie L.
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| Note
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Place of publication: United States, Ann Arbor; ISBN=978-1-339-79797-7
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| Abstract
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Groundwater contamination assessment can be useful in taking proper actions during the environmental emergency. Traditional contaminant transport models, along with the stochastic filtering techniques, can be a useful tool to predict the contaminant movement accurately. A three-dimensional deterministic model was taken into consideration to simulate the advective-diffusive transport of non-conservative contaminant in groundwater. Multiple stochastic data assimilation techniques, Ensemble Kalman Filter (EnKF), Local Ensemble Transform Kalman Filter (LETKF), and the global form of the LETKF, denoted as GETKF were applied to the model. The groundwater contaminant concentration was predicted for a certain simulation period within a particular domain. The performance of the multiple data assimilation techniques was measured by using the root-mean-square-error (RMSE), Mean absolute error (MAE), and R-squared equations. The results show that data assimilation significantly improved the prediction of contaminant concentration. The EnKF method reduced the root-mean-square-error (RMSE) of the contaminant prediction from 12.5 mg/L to 1.31 mg/L whereas the LETKF and GETKF reduced that to 0.46 mg/L and 0.38 mg/L, respectively. The EnKF, LETKF and GETKF improved prediction by 89.48%, 96.30% and 96.82%, respectively. MAE and R-squared analysis confirmed that stochastic techniques performed better than the deterministic technique. The sensitivity tests suggest that these data assimilation techniques are very sensitive to the observation noise, process noise, and ensemble size.
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| Subject
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Civil engineering; Water Resource Management; Environmental engineering
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| Descriptor
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Applied sciences;Earth sciences;Data assimilation;Ensemble kalman filter;GETKF;Groundwater contamination;LETKF;Modeling
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| Added Entry
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Chang, Shoou-Yuh
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| Added Entry
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Civil EngineeringNorth Carolina Agricultural and Technical State University
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