http://www.seipub.org/awrp/RSS.aspxen-US2016-0<p class="abstract">Groundwater Contaminant Transport Modeling Using Multiple Adaptive Data Assimilation Techniques</p><ul><li>Pages 1-20</li><li>Author Elvis Boamah AddaiShoou-Yuh ChangGodwin Appiah AssumaningAmirul Islam Raji</li><li>Abstract In modeling the behavior of contaminants in a subsurface environment using data assimilation schemes, accurate assignment of model and observation errors are significant for the successful application of the techniques. In this study, a three-dimensional transport model was used to simulate the advection and dispersiontransport of contaminant in the subsurface. Stochastic data assimilation schemes were coupled with the subsurface contaminant transport model to predict the state of the contaminant. Three data assimilation techniques namely the conventional Ensemble KalmanFilter, the Adaptive Ensemble Kalman Filter and the Hybrid Adaptive Ensemble Kalman Filter were adopted to improve the prediction of the contaminant fate and transport in the groundwater. The Ensemble Kalman Filter applies a Monte Carlo approach to the filtering problem. The adaptive filtering technique employs the diagnostic approach to fine tune the model and observation covariance matrix. The hybrid technique uses combination of the forecast covariance matrix and the invariant background covariance matrix to explore the Ensemble Kalman filter. The impact of the filters on the numerical model is examined by using the Normalized Root Mean Square Error (NRMSE), Average Absolute Bias (AAB) metric, and Maximum Absolute Deviation (MAD) techniques. The AAB evaluation of Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter shows error reduction of 93% and 90%, respectively, while the MAD assessment recorded 94% and 89% improvement respectively, in the numerical model. The results of simulations show that the prediction accuracy of the filters is better than numerical model. The proposed Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter, takes advantage of the adaptive factor and the weighting factor, respectively to improve the prediction efficiency of the Ensemble Kalman filter. Sensitivity analysis performed on Adaptive Ensemble Kalman Filter and Hybrid Adaptive Ensemble Kalman Filter using NRMSE indicates that the adaptive factor does not affect the prediction accuracy of the former whereas the weighting factor has influence on the later.</li></ul>http://www.seipub.org/awrp/PaperInfo.aspx?ID=31844Advances in Water Resource and Protectionhttp://www.seipub.org/awrp/PaperInfo.aspx?ID=31844