عنوان

Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling

شماره

TL48810

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Use of Multiple Data Assimilation Techniques in Groundwater Contaminant Transport Modeling

نام عام مواد

[Thesis]

نام نخستين پديدآور

Amirul Islam Rajib

نام ساير پديدآوران

Chang, Shoou-Yuh

نام ناشر، پخش کننده و غيره

North Carolina Agricultural and Technical State University

تاریخ نشرو بخش و غیره

2016

نام خاص و کميت اثر

76

متن يادداشت

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

متن يادداشت

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

جزئيات پايان نامه و نوع درجه آن

M.S.

نظم درجات

Civil Engineering

کسي که مدرک را اعطا کرده

North Carolina Agricultural and Technical State University

امتياز متن

2016

متن يادداشت

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.

موضوع مستند نشده

Civil engineering; Water Resource Management; Environmental engineering

اصطلاح موضوعی

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

مستند نام اشخاص تاييد نشده

Islam, Mohammad Moshfiqul

مستند نام اشخاص تاييد نشده

Chang, Shoou-Yuh

تقسيم فرعي

Civil Engineering

مستند نام تنالگان تاييد نشده

North Carolina Agricultural and Technical State University

شماره بازیابی

1803936543; 10118466

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y