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dc.contributor.authorFernández, Betsaida-
dc.date.accessioned2017-06-09T07:11:51Z-
dc.date.available2017-06-09T07:11:51Z-
dc.date.issued2016de
dc.identifier.other495386111-
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/9170-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-91708de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-9153-
dc.description.abstractCalibration 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.en
dc.language.isoende
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc624de
dc.titleAutomated calibration for numerical models of riverflowen
dc.title.alternativeAutomatische Kalibrierung für numerische Flussmodellede
dc.typemasterThesisde
ubs.fakultaetBau- und Umweltingenieurwissenschaftende
ubs.institutInstitut für Wasser- und Umweltsystemmodellierungde
ubs.publikation.seitenxii, 118de
ubs.publikation.typAbschlussarbeit (Master)de
Enthalten in den Sammlungen:02 Fakultät Bau- und Umweltingenieurwissenschaften

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BF_Automated_Calibration.pdfMaster Thesis8,93 MBAdobe PDFÖffnen/Anzeigen
AutomatedCalibrationPoster.pdfPoster1,31 MBAdobe PDFÖffnen/Anzeigen


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