Traffic Operation Risk Assessments of Large-Scale Activities Based on Fuzzy Bayesian Network

2021 ◽  
Author(s):  
Dongdong Geng ◽  
Jian Lu ◽  
Ling Shen ◽  
Zhengqi Yan ◽  
Ling Dai
Keyword(s):  
Bayesian Network ◽  
Large Scale ◽  
Risk Assessments ◽  
Operation Risk ◽  
Traffic Operation

scholarly journals The role of spatial dependence for large-scale flood risk estimation

2020 ◽  
Vol 20 (4) ◽  
pp. 967-979 ◽  
Author(s):  
Ayse Duha Metin ◽  
Nguyen Viet Dung ◽  
Kai Schröter ◽  
Sergiy Vorogushyn ◽  
Björn Guse ◽  
...  
Keyword(s):  
Flood Risk ◽  
Spatial Dependence ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Cumulative Distribution ◽  
Risk Assessments ◽  
Return Periods ◽  
Risk Estimates ◽  
Flood Peaks ◽  
Complete Dependence

Abstract. Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100 % for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.

scholarly journals A Two-Dimension Dynamic Bayesian Network for Large-Scale Degradation Modeling with an Application to a Bridges Network

2017 ◽  
Vol 32 (8) ◽  
pp. 641-656 ◽  
Author(s):  
Alex Kosgodagan-Dalla Torre ◽  
Thomas G. Yeung ◽  
Oswaldo Morales-Nápoles ◽  
Bruno Castanier ◽  
Johan Maljaars ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Large Scale ◽  
Dynamic Bayesian Network ◽  
Two Dimension ◽  
Degradation Modeling

scholarly journals Dynamic Programming Structure Learning Algorithm of Bayesian Network Integrating MWST and Improved MMPC

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ruo-Hai Di ◽  
Ye Li ◽  
Ting-Peng Li ◽  
Lian-Dong Wang ◽  
Peng Wang
Keyword(s):  
Dynamic Programming ◽  
Bayesian Network ◽  
Spanning Tree ◽  
Large Scale ◽  
Structure Learning ◽  
Learning Algorithm ◽  
Maximum Weight ◽  
Significance Level ◽  
Bayesian Network Structure ◽  
Parents And Children

Dynamic programming is difficult to apply to large-scale Bayesian network structure learning. In view of this, this article proposes a BN structure learning algorithm based on dynamic programming, which integrates improved MMPC (maximum-minimum parents and children) and MWST (maximum weight spanning tree). First, we use the maximum weight spanning tree to obtain the maximum number of parent nodes of the network node. Second, the MMPC algorithm is improved by the symmetric relationship to reduce false-positive nodes and obtain the set of candidate parent-child nodes. Finally, with the maximum number of parent nodes and the set of candidate parent nodes as constraints, we prune the parent graph of dynamic programming to reduce the number of scoring calculations and the complexity of the algorithm. Experiments have proved that when an appropriate significance level α is selected, the MMPCDP algorithm can greatly reduce the number of scoring calculations and running time while ensuring its accuracy.

scholarly journals Mutational interactions define novel cancer subgroups

2017 ◽  
Author(s):  
Jack Kuipers ◽  
Thomas Thurnherr ◽  
Giusi Moffa ◽  
Polina Suter ◽  
Jonas Behr ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Cancer Progression ◽  
Large Scale ◽  
De Novo ◽  
Drug Repositioning ◽  
Tissue Type ◽  
Survival Prediction ◽  
Cancer Type ◽  
Cancer Data ◽  
Cancer Types

Large-scale genomic data can help to uncover the complexity and diversity of the molecular changes that drive cancer progression. Statistical analysis of cancer data from different tissues of origin highlights differences and similarities which can guide drug repositioning as well as the design of targeted and precise treatments. Here, we developed an improved Bayesian network model for tumour mutational profiles and applied it to 8,198 patient samples across 22 cancer types from TCGA. For each cancer type, we identified the interactions between mutated genes, capturing signatures beyond mere mutational frequencies. When comparing mutation networks, we found genes which interact both within and across cancer types. To detach cancer classification from the tissue type we performed de novo clustering of the pancancer mutational profiles based on the Bayesian network models. We found 22 novel clusters which significantly improved survival prediction beyond clinical and histopathological information. The models highlight key gene interactions for each cluster that can be used for genomic stratification in clinical trials and for identifying drug targets within strata.

scholarly journals Brief Communication: Critical Infrastructure impacts of the 2021 mid-July western European flood event

2021 ◽  
Author(s):  
Elco Koks ◽  
Kees Van Ginkel ◽  
Margreet Van Marle ◽  
Anne Lemnitzer
Keyword(s):  
The Netherlands ◽  
Flood Risk ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Critical Infrastructure ◽  
Risk Assessments ◽  
Flood Events ◽  
Infrastructure Systems ◽  
River Flood ◽  
Sewage Systems

Abstract. Germany, Belgium and The Netherlands were hit by extreme precipitation and flooding in July 2021. This Brief Communication provides an overview of the impacts to large-scale critical infrastructure systems and how recovery has progressed during the first six months after the event. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. Impacts range from completely destroyed bridges and sewage systems, to severely damaged schools and hospitals. We find that large-scale risk assessments, often focused on larger (river) flood events, do not find these local, but severe, impacts. This may be the result of limited availability of validation material. As such, this study will not only help to better understand how critical infrastructure can be affected by flooding, but can also be used as validation material for future flood risk assessments.

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