A Bayesian stochastic generator to complement existing climate change scenarios: Supporting uncertainty quantification in marine and coastal ecosystems

Available climate change projections, which can be used for quantifying future changes in marine and coastal ecosystems, usually consist of a few scenarios. Studies addressing ecological impacts of climate change often make use of a low- (RCP2.6), moderate- (RCP4.5) or high climate scenario (RCP8.5), without taking into account further...

Bayesian Inference of Piping Model Uncertainties Based on Field Observations

This paper presents a Bayesian model to determine the model uncertainty of a critical horizontal gradient model for piping for dikes, such a Lane and Bligh. A Bayesian model is needed for two reasons. First, there is a large overlap in cases that failed and survived. Second, the evidence of the failed cases is limited .The model consists of a...

Integral Failure Analysis of Pipelines in Flood Defenses

The presence of pipelines in flood defenses presents a challenge for assessing the probability of flooding. To prevent costly structural measures compensating for the possible negative effects of pipeline failure, we developed a framework for an integral failure analysis of pipeline and dike failure. The assessment framework is based on an event...

Urban building energy modeling using a 3D city model and minimizing uncertainty through Bayesian inference: A case study focuses on Amsterdam residential heating demand simulation

To cut down immense greenhouse gases emission and energy consumption in the rapidly urbanizing world, a holistic understanding and rethinking of our dynamic urban energy system are inevitable. Performing bottom-up building energy modelings at urban scale based on Geographic Information System (GIS) and semantic 3D city models could be a...

Data assimilation for Navier-Stokes using the least-squares finite-element method

We investigate theoretically and numerically the use of the least-squares finite-element method (LSFEM) to approach data-assimilation problems for the steady-state, incompressible Navier-Stokes equations. Our LSFEM discretization is based on a stress-velocity-pressure (S-V-P) first-order formulation, using discrete counterparts of the Sobolev...

Bayesian and Dempster–Shafer reasoning for knowledge-based fault diagnosis: A comparative study

Even though various frameworks exist for reasoning under uncertainty, a realistic fault diagnosis task does not fit into any of them in a straightforward way. For each framework, only part of the available data and knowledge is in the desired format. Moreover, additional criteria, like clarity of inference and computational efficiency,...