Online

Howard, Lucas J.

Civil and Environmental Engineering

2016

MS

Process-based models are used in a diverse array of fields, including environmental engineering to provide supporting information to engineers, policymakers and stakeholdes. Recent advances in remote sensing and data storage technology have provided opportunities for improving the application of process-based models and visualizing data, but also present new challenges. The availability of larger quantities of data may allow models to be constructed and calibrated in a more thorough and precise manner, but depending on the type and volume of data, it is not always clear how to incorporate the information content of these data into a coherent modeling framework. In this context, using process-based models in new ways to provide decision support or to produce more complete and flexible predictive tools is a key task in the modern data-rich engineering world. In standard usage, models can be used for simulating specific scenarios; they can also be used as part of an automated design optimization algorithm to provide decision support or in a data-assimilation framework to incorporate the information content of ongoing measurements. In that vein, this thesis presents and demonstrates extensions and refinements to leverage the best of what process-based models offer using Differential Evolution (DE) the Extended Kalman Filter (EKF). Coupling multi-objective optimization to a process-based model may provide valuable information provided an objective function is constructed appropriately to reflect the multi-objective problem and constraints. That, in turn, requires weighting two or more competing objectives in the early stages of an analysis. The methodology proposed here relaxes that requirement by framing the model optimization as a sensitivity analysis. For demonstration, this is implemented using a surface water model (HEC-RAS) and the impact of floodplain access up and downstream of a fixed bridge on bridge scour is analyzed. DE, an evoutionary global optimization algorithm, is wrapped around a calibrated HEC-RAS model. Multiple objective functions, representing different relative weighting of two objectives, are used; the resulting rank-orders of river reach locations by floodplain access sensitivity are consistent across these multiple functions. To extend the applicability of data assimilation methods, this thesis proposes relaxing the requirement that the model be calibrated (provided the parameters are still within physically defensible ranges) before performing assimilation. The model is then dynamically calibrated to new state estimates, which depend on the behavior of the model. Feasibility is demonstrated using the EKF and a synthetic dataset of pendulum motion. The dynamic calibration method reduces the variance of prediction errors compared to measurement errors using an initially uncalibrated model and produces estimates of calibration parameters that converge to the true values. The potential application of the dynamic calibration method to river sediment transport modeling is proposed in detail, including a method for automated calibration using sediment grain size distribution as a calibration parameter.