Updatable Spatio-Temporal Probabilistic Corrosion Modeling for Offshore Structures

2018 ◽  
Author(s):  
Karoline M. Neumann ◽  
Ole Tom Vårdal ◽  
Sören Ehlers
Keyword(s):  
Probabilistic Models ◽  
Offshore Structures ◽  
Current State ◽  
Time Space ◽  
Temporal Models ◽  
Corrosion Model ◽  
Practical Engineering ◽  
Industry Practice ◽  
Spatio Temporal ◽  
Corrosive Environments

Corrosion models are important to assess how the corrosion influences current and future structural strength. For this purpose it is desirable to describe the uneven corrosion diminution of the irregular surface (i.e. space) and progression (i.e. time) in various corrosive environments. Thickness measurements give an indication of the current state, and should be considered in the corrosion model. The inherit uncertainty in corrosion argues for a probabilistic type of corrosion model. Probabilistic models to describe corrosion in time and space, and that can be updated with observations exist, but are typically too complicated for practical engineering use for in-service corrosion assessment. Simpler models exist, that do not describe all of the mentioned aspects (probabilistic, updatable, describe corrosion in time, space and various environments). Here, a simple model covering these aspects is described in two parts. First bayes updating is used to estimate the parameters of the corrosion distribution for each unique environment. The second part uses this resulting distribution and describes how this distribution develops with time. The model is demonstrated with an example and compared to similar spatio-temporal models. The model is promising for improvement from simplistic uniform description of surface and linear progression used in current industry practice.

scholarly journals Spatio-temporal variograms and covariance models

2005 ◽  
Vol 37 (3) ◽  
pp. 706-725 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Random Field ◽  
Covariance Function ◽  
Time Lag ◽  
Dependent Data ◽  
Covariance Functions ◽  
Time Space ◽  
Temporal Models ◽  
Covariance Models ◽  
Spatio Temporal ◽  
Over Time

Variograms and covariance functions are the fundamental tools for modeling dependent data observed over time, space, or space-time. This paper aims at constructing nonseparable spatio-temporal variograms and covariance models. Special attention is paid to an intrinsically stationary spatio-temporal random field whose covariance function is of Schoenberg-Lévy type. The correlation structure is studied for its increment process and for its partial derivative with respect to the time lag, as well as for the superposition over time of a stationary spatio-temporal random field. As another approach, we investigate the permissibility of the linear combination of certain separable spatio-temporal covariance functions to be a valid covariance, and obtain a subclass of stationary spatio-temporal models isotropic in space.

scholarly journals Spatio-temporal variograms and covariance models

2005 ◽  
Vol 37 (03) ◽  
pp. 706-725 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Random Field ◽  
Covariance Function ◽  
Time Lag ◽  
Dependent Data ◽  
Covariance Functions ◽  
Time Space ◽  
Temporal Models ◽  
Covariance Models ◽  
Spatio Temporal ◽  
Over Time

Variograms and covariance functions are the fundamental tools for modeling dependent data observed over time, space, or space-time. This paper aims at constructing nonseparable spatio-temporal variograms and covariance models. Special attention is paid to an intrinsically stationary spatio-temporal random field whose covariance function is of Schoenberg-Lévy type. The correlation structure is studied for its increment process and for its partial derivative with respect to the time lag, as well as for the superposition over time of a stationary spatio-temporal random field. As another approach, we investigate the permissibility of the linear combination of certain separable spatio-temporal covariance functions to be a valid covariance, and obtain a subclass of stationary spatio-temporal models isotropic in space.

scholarly journals Pyramidal Framework: Guidance for the Next Generation of GIS Spatial-Temporal Models

2021 ◽  
Vol 10 (3) ◽  
pp. 188
Author(s):  
Cyril Carré ◽  
Younes Hamdani
Keyword(s):  
Conceptual Models ◽  
Future Generations ◽  
Temporal Data ◽  
Temporal Modeling ◽  
Temporal Models ◽  
Gis Data ◽  
Spatio Temporal ◽  
New Interpretation ◽  
Conventional Literature

Over the last decade, innovative computer technologies and the multiplication of geospatial data acquisition solutions have transformed the geographic information systems (GIS) landscape and opened up new opportunities to close the gap between GIS and the dynamics of geographic phenomena. There is a demand to further develop spatio-temporal conceptual models to comprehensively represent the nature of the evolution of geographic objects. The latter involves a set of considerations like those related to managing changes and object identities, modeling possible causal relations, and integrating multiple interpretations. While conventional literature generally presents these concepts separately and rarely approaches them from a holistic perspective, they are in fact interrelated. Therefore, we believe that the semantics of modeling would be improved by considering these concepts jointly. In this work, we propose to represent these interrelationships in the form of a hierarchical pyramidal framework and to further explore this set of concepts. The objective of this framework is to provide a guideline to orient the design of future generations of GIS data models, enabling them to achieve a better representation of available spatio-temporal data. In addition, this framework aims at providing keys for a new interpretation and classification of spatio-temporal conceptual models. This work can be beneficial for researchers, students, and developers interested in advanced spatio-temporal modeling.

scholarly journals Spatio-temporal expectile regression models

2019 ◽  
Vol 20 (4) ◽  
pp. 386-409
Author(s):  
Elmar Spiegel ◽  
Thomas Kneib ◽  
Fabian Otto-Sobotka
Keyword(s):  
Tensor Product ◽  
Least Squares Regression ◽  
Expectile Regression ◽  
Specific Distribution ◽  
Weighted Means ◽  
Temporal Models ◽  
Error Terms ◽  
Spatio Temporal ◽  
Set Up ◽  
Main Effects

Spatio-temporal models are becoming increasingly popular in recent regression research. However, they usually rely on the assumption of a specific parametric distribution for the response and/or homoscedastic error terms. In this article, we propose to apply semiparametric expectile regression to model spatio-temporal effects beyond the mean. Besides the removal of the assumption of a specific distribution and homoscedasticity, with expectile regression the whole distribution of the response can be estimated. For the use of expectiles, we interpret them as weighted means and estimate them by established tools of (penalized) least squares regression. The spatio-temporal effect is set up as an interaction between time and space either based on trivariate tensor product P-splines or the tensor product of a Gaussian Markov random field and a univariate P-spline. Importantly, the model can easily be split up into main effects and interactions to facilitate interpretation. The method is presented along the analysis of spatio-temporal variation of temperatures in Germany from 1980 to 2014.

scholarly journals A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models

2015 ◽  
Vol 57 (3) ◽  
pp. 325-345 ◽  
Author(s):  
Su Yun Kang ◽  
James McGree ◽  
Peter Baade ◽  
Kerrie Mengersen
Keyword(s):  
Cancer Incidence ◽  
Temporal Models ◽  
Spatio Temporal

scholarly journals Spatiotemporal mapping and detection of mortality cluster due to cardiovascular disease with Bayesian hierarchical framework using integrated nested Laplace approximation: A discussion of suitable statistic applications in Kersa, Oromia, Ethiopia

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Melkamu Dedefo ◽  
Henry Mwambi ◽  
Sileshi Fanta ◽  
Nega Assefa
Keyword(s):  
Cause Of Death ◽  
Linear Models ◽  
Temporal Distribution ◽  
Mortality Data ◽  
Contributing Factors ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Non Parametric

Cardiovascular diseases (CVDs) are the leading cause of death globally and the number one cause of death globally. Over 75% of CVD deaths take place in low- and middle-income countries. Hence, comprehensive information about the spatio-temporal distribution of mortality due to cardio vascular disease is of interest. We fitted different spatio-temporal models within Bayesian hierarchical framework allowing different space-time interaction for mortality mapping with integrated nested Laplace approximations to analyze mortality data extracted from the health and demographic surveillance system in Kersa District in Hararege, Oromia Region, Ethiopia. The result indicates that non-parametric time trends models perform better than linear models. Among proposed models, one with non-parametric trend, type II interaction and second order random walk but without unstructured time effect was found to perform best according to our experience and. simulation study. An application based on real data revealed that, mortality due to CVD increased during the study period, while administrative regions in northern and south-eastern part of the study area showed a significantly elevated risk. The study highlighted distinct spatiotemporal clusters of mortality due to CVD within the study area. The study is a preliminary assessment step in prioritizing areas for further and more comprehensive research raising questions to be addressed by detailed investigation. Underlying contributing factors need to be identified and accurately quantified.

scholarly journals Legal doctrine, legal science and protection of human rights (efficiency ratio)

2020 ◽  
Vol 11 (11) ◽  
pp. 11-15
Author(s):  
Onischenko N.
Keyword(s):  
Human Rights ◽  
Civil Society ◽  
Rule Of Law ◽  
Legal Doctrine ◽  
The Rule Of Law ◽  
Current State ◽  
Legal Science ◽  
Democratic State ◽  
Protection Of Human Rights ◽  
Spatio Temporal

The current state of Ukrainian society requires the scientific community to find answers to the general social impulses needed for reform strategies: from unconstructive attempts to replace the state with civil society to efforts aimed at their balanced interaction. It is the principle of social and political balance in the relationship between the rule of law and civil society should be the basis for correcting and correcting the unstable economic situation, overcoming acute social conflicts, establishing the rule of law, building a democratic state. It should be noted that it is quite clear that sometimes the implementation of a right requires at least not one, but clearly several opportunities, such as: economic, educational, social, gender, etc., existing in the relevant spatio-temporal continuum. Moreover, there is an indisputable thesis that there are no secondary or non-first-class or type of human rights, so every unrealized, not realized in time or not fully realized right, without a doubt, is based on the lack of, first of all, the corresponding real opportunities. It is also clear that the implementation of a certain right depends, for example, on the relevant regulations. we note that state-building processes, their dynamics, progressive trends depend on many factors. In this context, the interconnectedness of the development of a democratic, legal, social, European state and the formation of a mature, active, civil society was considered. Keywords: legal science, legal doctrine, human rights, rights and opportunities, legal development.

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