02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
Browse
3 results
Search Results
Item Open Access Automated calibration for numerical models of riverflow(2016) Fernández, BetsaidaCalibration of numerical models is fundamental since the beginning of all types of hydro system modeling, to approximate the parameters that can mimic the overall system behavior. Thus, an assessment of different deterministic and stochastic optimization methods is undertaken to compare their robustness, computational feasibility, and global search capacity. Also, the uncertainty of the most suitable methods is analyzed. These optimization methods minimize the objective function that comprises synthetic measurements and simulated data. Synthetic measurement data replace the observed data set to guarantee an existing parameter solution. The input data for the objective function derivate from a hydro-morphological dynamics numerical model which represents an 180-degree bend channel. The hydro- morphological numerical model shows a high level of ill-posedness in the mathematical problem. The minimization of the objective function by different candidate methods for optimization indicates a failure in some of the gradient-based methods as Newton Conjugated and BFGS. Others reveal partial convergence, such as Nelder-Mead, Polak und Ribieri, L-BFGS-B, Truncated Newton Conjugated, and Trust-Region Newton Conjugated Gradient. Further ones indicate parameter solutions that range outside the physical limits, such as Levenberg-Marquardt and LeastSquareRoot. Moreover, there is a significant computational demand for genetic optimization methods, such as Differential Evolution and Basin-Hopping, as well as for Brute Force methods. The Deterministic Sequential Least Square Programming and the scholastic Bayes Inference theory methods present the optimal optimization results.Item Open Access Development of updated AWARE characterization factors for water scarcity footprinting using and comparing different hydrological datasets(2022) Seitfudem, GeorgThe Available WAter REmaining (AWARE) method is a widely accepted tool for assessing the impact of water consumption on other water users in the context of Life Cycle Assessment (LCA). It is based on the evaluation of runoff data obtained via global hydrological models. In preparation for a future revision, this thesis collects suggestions for further development of the calculation of AWARE characterization factors (CFs). The emphasis is on (i) increased precision of area values, (ii) the exclusion of irrelevant watersheds, and (iii) the calculation of the Environmental Water Requirements (EWRs) from the discharge input data. While the watershed exclusion has no effect on the remaining watersheds, the other modifications result in varying degrees of change in the CFs depending on the sensitivity of the watershed under consideration. The second part of this paper examines CFs calculated from different climate data inputs to hydrologic modeling. Observation-based climate data leads to less similar CFs than simulation-based data. The uncertainty in the observation-based climate data probably must be attributed to the simulation-based data, too, due to the strong link to the bias correction. Therefore, the uncertainty regarding observational data presumably contributes significantly to the overall uncertainty of the simulation-based data, and the selection of the bias-correction dataset could be more influential to the AWARE CFs than the selection of individual climate simulations.Item Open Access Bayesian Model Selection for hydro-morphodynamic models(2017) Mohammadi, FaridA good grasp of hydro-morphodynamic processes plays a major role in modern river management to accommodate its often-conflicting functions. In the last century, a variety of models has been developed to improve our perception of sediment transport and the resulting changes in river bed topography, using several empirical formulations. Therefore, there is a demonstrated need to establish a framework that helps the river engineer to select the closest model to the measurements. This study suggested a Bayesian Model Selection (BMS) framework to direct the modeler towards the most robust and sensible representation of the hydro-morphodynamic conditions of the river under investigation. The proposed framework employs Bayesian Model Evidence (BME) resulting from Bayesian Model Averaging (BMA) as a model evaluation yardstick for ranking competing models. BMA performs a compromise between bias and variance, i.e. it blends a measure for goodness of fit with a penalty for unacceptable model complexity. This approach requires many model simulations, which are computationally expensive. However, this issue can be diminished by a mathematically optimal response surface via the aPC technique projects the original model. This response surface, also known as a reduced (surrogate) model can exhibit the reliance of the model on all relevant parameters for calibration at high order accuracy. The proposed framework was implemented in the model selection of two test cases; namely a test case model, based on an experiment done by Yen and Lee (1995) and a river model of a 10-km stretch of the lower Rhine, provided by the FederalWaterways Research Institute (BAW) in Karlsruhe. The results demonstrated that the proposed framework was acceptably able to detect the most desirable model in which a good agreement existed between the simulation results and measurement data when the complete knowledge of initial parameters lacked. Further, the BMS framework could direct us to the most probable parameter regions for the task of optimization via probability density distributions of uncertain variables. Overall, this research fills a void in the literature with respect to the selection of sediment transport equation for representation of hydro-morphodynamics of natural rivers. The suggested approach provides an objective guidance in the model selection to assist even less experienced users by reducing the professional expertise required for further optimization tasks.