عنوان

Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling

Number

TL48810

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling

General Material Designation

[Thesis]

First Statement of Responsibility

Amirul Islam Rajib

Subsequent Statement of Responsibility

Chang, Shoou-Yuh

Name of Publisher, Distributor, etc.

North Carolina Agricultural and Technical State University

Date of Publication, Distribution, etc.

2016

Specific Material Designation and Extent of Item

76

Text of Note

Committee members: Jha, Manoj K.; Teasley, Stephanie L.

Text of Note

Place of publication: United States, Ann Arbor; ISBN=978-1-339-79797-7

Dissertation or thesis details and type of degree

M.S.

Discipline of degree

Civil Engineering

Body granting the degree

North Carolina Agricultural and Technical State University

Text preceding or following the note

2016

Text of Note

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.

Civil engineering; Water Resource Management; Environmental engineering

Subject Term

Applied sciences;Earth sciences;Data assimilation;Ensemble kalman filter;GETKF;Groundwater contamination;LETKF;Modeling

Islam, Mohammad Moshfiqul

Chang, Shoou-Yuh

Subdivision

Civil Engineering

North Carolina Agricultural and Technical State University

Call Number

1803936543; 10118466

Electronic name

p

[Thesis]

276903

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