No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

Accurate hydrologic forecasting is vital for proper water resource management. Practices that are impacted by these forecasts include power generation, reservoir management, agricultural water use, and flood early warning systems. Despite these needs, the models largely used are simplifications of the real world and are therefore imperfect. The forecasters face other challenges in addition to the model uncertainty, which includes imperfect observations used for model calibration and validation, imperfect meteorological forecasts, and the ability to effectively communicate forecast results to decision-makers. Bayesian methods are commonly used to address some of these issues, and this thesis will be focused on improving methods related to recursive Bayesian estimation, more commonly known as data assimilation. Data assimilation is a means to optimally account for the uncertainties in observations, models, and forcing data. In the literature, data assimilation for urban hydrologic and flood forecasting is rare; therefore the main areas of study in this thesis are urban and semi-urban watersheds. By providing improvements to data assimilation methods, both hydrologic and flood forecasting can be enhanced in these areas. This work explored the use of alternative data products as a type of observation that can be assimilated to improve hydrologic forecasting in an urban watershed. The impact of impervious surfaces in urban and semi-urban watersheds was also evaluated in regards to its impact on remotely sensed soil moisture assimilation. Lack of observations is another issue when it comes to data assimilation, particularly in semi- or fully-distributed models; because of this, an improved method for updating locations which do not have observations was developed which utilizes information theory’s mutual information. Finally, we explored extending data assimilation into the short-term forecast by using prior knowledge of how a model will respond to forecasted forcing data. Results from this work found that using alternative data products such as those from the Snow Data Assimilation System or the Soil Moisture and Ocean Salinity mission, can be effective at improving hydrologic forecasting in urban watersheds. They also were effective at identifying a limiting imperviousness threshold for soil moisture assimilation into urban and semi-urban watersheds. Additionally, the inclusion of mutual information between gauged and ungauged locations in a semi-distributed hydrologic model was able to provide better state updates in models. Finally, by extending data assimilation into the short-term forecast, the reliability of the forecasts could be improved substantially. / Dissertation / Doctor of Philosophy (PhD) / The ability to accurately model hydrological systems is essential, as that allows for better planning and decision making in water resources management. The better we can forecast the hydrologic response to rain and snowmelt events, the better we can plan and manage our water resources. This includes better planning and usage of water for agricultural purposes, better planning and management of reservoirs for power generation, and better preparing for flood events. Unfortunately, hydrologic models primarily used are simplifications of the real world and are therefore imperfect. Additionally, our measurements of the physical system responses to atmospheric forcing can be prone to both systematic and random errors that need to be accounted for. To address these limitations, data assimilation can be used to improve hydrologic forecasts by optimally accounting for both model and observation uncertainties. The work in this thesis helps to further advance and improve data assimilation, with a focus on enhancing hydrologic forecasting in urban and semi-urban watersheds. The research presented herein can be used to provide better forecasts, which allow for better planning and decision making.

As the vehicle's autonomy level increases, new security systems are added to its functionality so accidents can be avoided. Those security systems can only be reliable and work effectively if an accurate estimation of the vehicle's velocity is available.  Given the importance of the estimation of velocity in vehicles, in this thesis, we used the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) to improve the velocity estimation of a heavy-duty dumper vehicle. Those methods were used to fuse the wheels' speed information and the Inertial Measurement Unit (IMU) readings available from the vehicle. A simulation model of the vehicle was created using Simulink which outputted the ground truth velocities that were used as a reference for comparison with the estimators when the vehicle went through different path patterns that included combinations of going straight, steering, and experiencing excessive wheel slip. Moreover, the sensors were simulated in Simulink as well and they provided the data that was used by the MATLAB scripts that coded the EKF and the UKF. The performance of the estimators was compared with the ground truth velocities by calculating the Root Mean Squared Error (RMSE) in each case. The results from the experiments showed that both the EKF and the UKF performed the same for the used simulation model, however, both improved the velocity estimation by decreasing the RMSE values from 0.46 (estimation using only IMU information) and 0.226 (estimation based only on wheels information) to 0.20. This is evidence that the Kalman Filter variations are a good option to test when the task is estimating the velocity of a vehicle.