The purpose of online parameter learning and modeling is to validate and restore the properties of a structure based on legitimate observations. Online parameter learning assists in determining the unidentified characteristics of a structure by offering enhanced predictions of the vibration responses of the system. From the utilization of modeling, the predicted outcomes can be produced with a minimal amount of given measurements, which can be compared to the true response of the system. In this simulation study, the Kalman filter technique is used to produce sets of predictions and to infer the stiffness parameter based on noisy measurement. From this, the performance of online parameter identification can be tested with respect to different noise levels. This research is based on simulation work showcasing how effective the Kalman filtering techniques are in dealing with analytical uncertainties of data. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

This thesis has investigated the improvement of forecasting temperature in a coastal embayment through the assimilation of sea surface temperature (SST) observations. The research was prompted by the increasing pressures on the coastal marine environment. To better manage the environment, an improved understanding of its future state is necessary. Improving the forecasting of temperature advances our knowledge in this direction. Whilst assimilation of SST is routinely carried out for oceans, its use has been minimal in coastal regions, which is more complicated because of anisotropic covariances and a breakdown of geostrophy in the coastal region. Improvements in computing power, and the introduction of ensemble-based assimilation techniques have made the approach followed in this thesis possible. (For complete abstract open document)