scholarly journals VEGETATED HYDRODYNAMIC SYSTEM: PARAMETERIZATION AND STOCHASTIC DEPENDENCE MODELLING

2020 ◽  
pp. 25
Author(s):  
Muhammad Hassan Khan Niazi ◽  
Oswaldo Morales Nápoles ◽  
Bregje K. Van Wesenbeeck
Keyword(s):  
Numerical Modeling ◽  
Flood Hazard ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Load Reduction ◽  
Wave Load ◽  
Stochastic Dependence ◽  
Dependence Modeling ◽  
Coastal Systems ◽  
Dependence Modelling

Vegetation as a nature-based solution for increasing flood risk has convincingly shown potential for flood hazard (wave load) reduction but lacks generalized results. In this study we have introduced stochastic dependence modeling using non-parametric Bayesian networks (NPBN) for vegetated coastal systems where the system was parametrized using continuous marginal distributions, and likely (conditional) correlations among variables. The model represented a consistent joint probability distribution and hence can be used to generate physically realistic conditions in data-scare environments. It adds value to numerical modeling by reducing the number of simulations required to get meaningful generalized results. Main findings, that were derived by using a NPBN, help to pave way for implementation of nature-based solutions for a range of realistic conditions that can be found across global coastal foreshores.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/T6TP0DH0qMw

scholarly journals ADVANCES AND ISSUES IN UNCERTAINTY QUANTIFICATION FOR COASTAL FLOOD HAZARDS

2018 ◽  
pp. 19
Author(s):  
Taylor G. Asher ◽  
Jennifer L. Irish ◽  
Donald T. Resio
Keyword(s):  
Tropical Cyclones ◽  
Historical Data ◽  
Flood Hazard ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Probability Method ◽  
Flood Hazards ◽  
Significant Cost ◽  
Flooding Risk ◽  
Flood Elevation

Probabilistic flood hazard assessments have advanced substantially, with modern methods for dealing with the risk from tropical cyclones utilizing either a variation of the joint probability method with optimal sampling (JPM-OS)2,3 or the statistical deterministic track method (SDTM)1,4. In the JPM-OS, tropical cyclones are reduced to a set of 5 to 9 parameters, whose characteristics are analyzed statistically to develop a joint probability distribution for tropical cyclones of given characteristics. In the SDTM, cyclogenesis of a large number of storms is seeded via a statistical model from historical data, then storms are propagated using one of several different methods, incorporating varying degrees of the physics of cyclone transformation as the storms propagate. Due to the significant cost of storm surge simulations, some form of optimization or selection is then performed to reduce the number of synthetic storms that must be simulated to determine the flood elevation corresponding to a given recurrence interval (e.g. the so-called 100-year flood). In both methods, substantial uncertainties exist, which have a tendency to increase the estimated flooding risk. Efforts to account for these uncertainties have varied, and there remains significant work to be done. Here, we demonstrate how these uncertainties tend to increase the flood risk and show that additional sources of uncertainty remain to be accounted for.

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale

2020 ◽  
Vol 20 (2) ◽  
pp. 489-504 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals Probability elicitation using geostatistics in hydrocarbon exploration

2021 ◽  
Author(s):  
André Luís Morosov ◽  
Reidar Brumer Bratvold
Keyword(s):  
Value Creation ◽  
Decision Process ◽  
Conceptual Models ◽  
Joint Probability ◽  
Decision Makers ◽  
Hydrocarbon Exploration ◽  
Joint Probability Distribution ◽  
Proof Of Concept ◽  
Geological Information ◽  
Decision Supporting

AbstractThe exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

scholarly journals A TWO-CLASS RETRIAL SYSTEM WITH COUPLED ORBIT QUEUES

2017 ◽  
Vol 31 (2) ◽  
pp. 139-179 ◽  
Author(s):  
Ioannis Dimitriou
Keyword(s):  
Performance Metrics ◽  
Kernel Method ◽  
Length Distribution ◽  
Probability Generating Function ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Single Server ◽  
Method Performance ◽  
Service Station ◽  
Queue Length Distribution

We consider a single server system accepting two types of retrial customers, which arrive according to two independent Poisson streams. The service station can handle at most one customer, and in case of blocking, typeicustomer,i=1, 2, is routed to a separate typeiorbit queue of infinite capacity. Customers from the orbits try to access the server according to the constant retrial policy. We consider coupled orbit queues, and thus, when both orbit queues are non-empty, the orbit queueitries to re-dispatch a blocked customer of typeito the main service station after an exponentially distributed time with rate μi. If an orbit queue empties, the other orbit queue changes its re-dispatch rate from μito$\mu_{i}^{\ast}$. We consider both exponential and arbitrary distributed service requirements, and show that the probability generating function of the joint stationary orbit queue length distribution can be determined using the theory of Riemann (–Hilbert) boundary value problems. For exponential service requirements, we also investigate the exact tail asymptotic behavior of the stationary joint probability distribution of the two orbits with either an idle or a busy server by using the kernel method. Performance metrics are obtained, computational issues are discussed and a simple numerical example is presented.

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