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dc.contributor.advisorHoteit, Ibrahim
dc.contributor.authorSawlan, Zaid A
dc.date.accessioned2012-12-12T06:46:11Z
dc.date.available2012-12-12T06:46:11Z
dc.date.issued2012-12
dc.identifier.citationSawlan, Z. A. (2012). Tsunami Prediction and Earthquake Parameters Estimation in the Red Sea. KAUST Research Repository. https://doi.org/10.25781/KAUST-92RB8
dc.identifier.doi10.25781/KAUST-92RB8
dc.identifier.urihttp://hdl.handle.net/10754/255453
dc.description.abstractTsunami concerns have increased in the world after the 2004 Indian Ocean tsunami and the 2011 Tohoku tsunami. Consequently, tsunami models have been developed rapidly in the last few years. One of the advanced tsunami models is the GeoClaw tsunami model introduced by LeVeque (2011). This model is adaptive and consistent. Because of different sources of uncertainties in the model, observations are needed to improve model prediction through a data assimilation framework. Model inputs are earthquake parameters and topography. This thesis introduces a real-time tsunami forecasting method that combines tsunami model with observations using a hybrid ensemble Kalman filter and ensemble Kalman smoother. The filter is used for state prediction while the smoother operates smoothing to estimate the earthquake parameters. This method reduces the error produced by uncertain inputs. In addition, state-parameter EnKF is implemented to estimate earthquake parameters. Although number of observations is small, estimated parameters generates a better tsunami prediction than the model. Methods and results of prediction experiments in the Red Sea are presented and the prospect of developing an operational tsunami prediction system in the Red Sea is discussed.
dc.language.isoen
dc.subjectTsunami Prediction and Earthquake
dc.subjectKalman Filter and Kalman Smoother
dc.subjectTsunami Predictions in the Red Sea
dc.subjectData Assimilation
dc.subjectParameter Estimation
dc.subjectEnsemble Kalman Filter
dc.titleTsunami Prediction and Earthquake Parameters Estimation in the Red Sea
dc.typeThesis
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberLaleg-Kirati, Taous-Meriem
dc.contributor.committeememberScavino, Marco
thesis.degree.disciplineApplied Mathematics and Computational Science
thesis.degree.nameMaster of Science


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