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Journal articles on the topic 'Probabilistic risk modelling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Śliwiński, Marcin, and Emilian Piesik. "Designing Control and Protection Systems with Regard to Integrated Functional Safety and Cybersecurity Aspects." Energies 14, no. 8 (April 16, 2021): 2227. http://dx.doi.org/10.3390/en14082227.

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This article addresses current problems of risk analysis and probabilistic modelling for functional safety management in the life cycle of safety-related systems. Two main stages in the lifecycle of these systems are distinguished, namely the design and operation. The risk analysis and probabilistic modelling differ in these stages in view of available knowledge and data. Due to the complexity and uncertainty involved, both qualitative and quantitative information can be useful in risk analysis and probabilistic modelling. Some methodological aspects of the functional safety assessment are outlined that include modelling of dependent failures or cybersecurity and verifying the safety integrity level (SIL) under uncertainty. It is illustrated how the assumptions in the process of risk analysis and probabilistic modelling influence results obtained and, therefore, potentially the decisions taken in functional safety management. Programmable control and safety systems play an important role in mitigating and controlling risks in the operation of hazardous installations. This paper presents ways to deal with safety hazards involving such systems to be considered in risk analysis and integrated functional safety and cybersecurity management.

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2

Basu, Asit P. "Probabilistic Risk Analysis." Technometrics 44, no. 4 (November 2002): 402. http://dx.doi.org/10.1198/tech.2002.s84.

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3

Melchers, Robert E., and Dan M. Frangopol. "Probabilistic modelling of structural degradation." Reliability Engineering & System Safety 93, no. 3 (March 2008): 363. http://dx.doi.org/10.1016/j.ress.2007.01.001.

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4

Brillinger, D. R., H. K. Preisler, and J. W. Benoit. "Probabilistic risk assessment for wildfires." Environmetrics 17, no. 6 (2006): 623–33. http://dx.doi.org/10.1002/env.768.

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5

Gómez-Fuster, José María, and Pilar Jiménez. "Probabilistic risk modelling for port investments: A practical approach." Case Studies on Transport Policy 8, no. 3 (September 2020): 822–31. http://dx.doi.org/10.1016/j.cstp.2020.06.001.

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6

Gray, George M. "Probabilistic Methods in Risk Analysis." Human and Ecological Risk Assessment: An International Journal 4, no. 2 (April 1998): 227–28. http://dx.doi.org/10.1080/10807039891284280.

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7

Stoudmann, Natasha, Bernd Nowack, and Claudia Som. "Prospective environmental risk assessment of nanocellulose for Europe." Environmental Science: Nano 6, no. 8 (2019): 2520–31. http://dx.doi.org/10.1039/c9en00472f.

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8

Thomsen, Rene O. "Dynamical Models in Geology: Sensitivity Analysis and Scientific Risk." Energy Exploration & Exploitation 11, no. 3-4 (July 1993): 329–56. http://dx.doi.org/10.1177/0144598793011003-406.

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Dynamical models are used routinely throughout various branches of geology and decisions are often based on the results of such models. Awareness of some fundamental limitations of any model used is therefore vital in order to avoid decision making based on highly uncertain results. Computer models are based on mathematical models which describe essential features of processes or system behaviour and a systematic approach to investigate and understand model limitations can therefore be applied. Sensitivity analysis provides a feel for the system response to uncertainties in both assumptions and observations. However, sensitivity analyses do not provide a feel for the level of confidence one should assign modelling results. A probabilistic approach to evaluation of uncertainties and modelling results is therefore demonstrated. Two cases are used as examples for the method and it is demonstrated how the combined sensitivity analyses and probabilistic evaluation greatly improve the use of even uncertain modelling results.

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Jiang, Xiaomo, Yong Yuan, and Xian Liu. "Multivariate probabilistic modelling for seepage risk assessment in tunnel segments." International Journal of Reliability and Safety 8, no. 2/3/4 (2014): 228. http://dx.doi.org/10.1504/ijrs.2014.069507.

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10

Stripling, Stuart, Michael Panzeri, Belen Blanco, Kate Rossington, Paul Sayers, and Alistair Borthwick. "Regional-scale probabilistic shoreline evolution modelling for flood-risk assessment." Coastal Engineering 121 (March 2017): 129–44. http://dx.doi.org/10.1016/j.coastaleng.2016.12.002.

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11

Welker, Christoph, Thomas Röösli, and David N. Bresch. "Comparing an insurer's perspective on building damages with modelled damages from pan-European winter windstorm event sets: a case study from Zurich, Switzerland." Natural Hazards and Earth System Sciences 21, no. 1 (January 25, 2021): 279–99. http://dx.doi.org/10.5194/nhess-21-279-2021.

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Abstract. With access to claims, insurers have a long tradition of being knowledge leaders on damages caused by windstorms. However, new opportunities have arisen to better assess the risks of winter windstorms in Europe through the availability of historic footprints provided by the Windstorm Information Service (Copernicus WISC). In this study, we compare how modelling of building damages complements claims-based risk assessment. We describe and use two windstorm risk models: an insurer's proprietary model and the open source CLIMADA platform. Both use the historic WISC dataset and a purposefully built, probabilistic hazard event set of winter windstorms across Europe to model building damages in the canton of Zurich, Switzerland. These approaches project a considerably lower estimate for the annual average damage (CHF 1.4 million), compared to claims (CHF 2.3 million), which originates mainly from a different assessment of the return period of the most damaging historic event Lothar–Martin. Additionally, the probabilistic modelling approach allows assessment of rare events, such as a 250-year-return-period windstorm causing CHF 75 million in damages, including an evaluation of the uncertainties. Our study emphasizes the importance of complementing a claims-based perspective with a probabilistic risk modelling approach to better understand windstorm risks. The presented open-source model provides a straightforward entry point for small insurance companies.

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12

Napiórkowski, Jerzy, and Jarosław Gonera. "Modelling Reliability of Henhouse Microclimate Systems with Risk Analysis Approaches." Agricultural Engineering 21, no. 3 (September 1, 2017): 47–60. http://dx.doi.org/10.1515/agriceng-2017-0024.

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AbstractThe paper presents the issue of modelling reliability of microclimate. It is very important to conduct the risk analysis in poultry farms in order to indicate the most dangerous elements of the discussed system in the aspect of maintaining usefulness. The main objective of the paper was to carry out the risk analysis for the system that maintains microclimate in a poultry farm. It was assumed in the paper that the ventilation system influences maintaining microclimate in a henhouse. Simultaneously, the most effective methods of risk reduction are indicated and a rational assessment of the safety level is made. Studies carried out in the paper enabled identification of the risk analysis approach which the best reflects the issue of maintaining technical equipment devices in operation. With the use of the matrix, the indicator and probabilistic approach, the exploitation risk related to the occurrence of previously defined failures of the henhouse ventilation system was assessed. Based on the results of analysis, a conclusion could have been made that concurrent results were obtained with the use of the matrix and probabilistic approaches. In these cases, for all failures the highest and the lowest exploitation risks of occurrence of certain failures in the ventilation system in a henhouse were the same.

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13

Willems, P., and J. Berlamont. "Probabilistic modelling of sewer system overflow emissions." Water Science and Technology 39, no. 9 (May 1, 1999): 47–54. http://dx.doi.org/10.2166/wst.1999.0439.

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The main advantages of the probabilistic modelling of urban drainage systems are the possibility of quantification and efficient reduction of the risk of ‘wrong’ management decisions or engineering designs, as well as the less biased mathematical modelling. Such probabilistic modelling achieves quantification of the uncertainty in the field of model-output. In the modelling of sewer system overflow emissions, a lot of uncertainties are involved: uncertainties about the spatial rainfall-input, model simplifications of the sewer system and runoff catchment, and biases and uncertainties about the model parameters. A methodology has been presented to quantify the different uncertainty-sources in a decomposed way by separating uncertainties about the model structure, the model input and the model parameters. It has been applied to an existing sewer system and WWTP, for which the emission modelling has been performed both by a full hydrodynamic and a simplified conceptual model. In this application, the importance of having accurate (spatial) rainfall data and a thoroughly calibrated runoff model has been noticed. A well-considered simplification of a full hydrodynamic model, for instance by a conceptual reservoir model, has a negligible influence on modelled values of sewer system emissions. Such simplification achieves a reduction of calculation times, which is a necessity in probabilistic modelling.

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Willems, P., and J. Berlamont. "Probabilistic emission and immission modelling: case-study of the combined sewer – WWTP – receiving water system at Dessel (Belgium)." Water Science and Technology 45, no. 3 (February 1, 2002): 117–24. http://dx.doi.org/10.2166/wst.2002.0068.

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The impact of the combined urban drainage and WWTP system of the village of Dessel (Belgium) on the Witte Nete receiving water is modelled both in terms of emissions and immissions. The hydrodynamic and water quality modelling is performed both in a deterministic and probabilistic way. For the deterministic modelling, detailed physically based and simplified conceptual models are used in a complementary way. In the probabilistic modelling, the different uncertainties in the deterministic model are classified in input uncertainties, parameter uncertainties and model-structure uncertainties. The probabilistic simulation results can be used in risk analysis and management, for the determination of the major uncertainty-sources and priorities in model improvement, for model bias elimination and for efficient model calibration.

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15

Ragas, Ad M. J. "Application of probabilistic modelling techniques in human and environmental risk assessment." Toxicology Letters 221 (August 2013): S21. http://dx.doi.org/10.1016/j.toxlet.2013.06.074.

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Oliver, J., X. S. Qin, H. Madsen, P. Rautela, G. C. Joshi, and G. Jorgensen. "A probabilistic risk modelling chain for analysis of regional flood events." Stochastic Environmental Research and Risk Assessment 33, no. 4-6 (May 11, 2019): 1057–74. http://dx.doi.org/10.1007/s00477-019-01681-3.

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17

Iqbal, Asif, Shirina Afroze, and Md Mizanur Rahman. "Vehicular PM Emissions and Urban Public Health Sustainability: A Probabilistic Analysis for Dhaka City." Sustainability 12, no. 15 (August 4, 2020): 6284. http://dx.doi.org/10.3390/su12156284.

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Particulate Matter (PM) pollution is generally considered as a prime indicator of urban air quality and is linked to human health hazards. As vehicles are a vital component of an urban setting, the risks of particulate pollution need to be assessed. An emission modelling is essential for that, and thus stochastic modelling approach involving Monte Carlo simulation technique was applied, aiming to reduce the uncertainty in emission modelling. The risks scenarios for the emissions were generated for 2019 (present state) and 2024 (future), integrating the probability of emissions and the associated AQI (Air Quality Index). Despite the vehicles being a minor source of PM in Dhaka (compared to the contribution from other sources), about one-third of the city is found under high risk due to the exhaust particulate pollution; having the potentiality to cover more than 60% of the city in the coming years, affecting the urban public health sustainability. However, the extent of implementation of planning and management strategies can revert the scenarios for the city, which can plausibly reduce the risk from 80% to 50%, or even to a no-risk state.

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18

Power, M., and L. S. McCarty. "Probabilistic Risk Assessment: Betting on its Future." Human and Ecological Risk Assessment: An International Journal 2, no. 1 (March 1996): 30–34. http://dx.doi.org/10.1080/10807039.1996.10387457.

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19

Jager, Tjalling, Henri A. den Hollander, Paul van der Poel, Mathieu G. J. Rikken, and Theo Vermeire. "Probabilistic Environmental Risk Assessment for Dibutylphthalate (DBP)." Human and Ecological Risk Assessment: An International Journal 7, no. 6 (November 2001): 1681–97. http://dx.doi.org/10.1080/20018091095302.

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20

Li, Haizhou, François Pinet, and Farouk Toumani. "Towards The Use of Probabilistic Spatial Relation Databases in Business Process Modeling." International Journal of Agricultural and Environmental Information Systems 6, no. 3 (July 2015): 50–62. http://dx.doi.org/10.4018/ijaeis.2015070104.

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In this paper, the authors combine the methods of probabilistic databases, GIS (Geographical Information Systems) and business process modelling to evaluate the agricultural activities. The authors provide a technique to evaluate the risk of agricultural activities among the hydrological objects (lakes, rivers, etc.) and agricultural plots. The spatial relation is an important information needed in the evaluation processes. This type of information is usually uncertain and the available data are often not precise. Consequently, probabilistic database is used to capture the uncertainty of the spatial objects in order to estimate the level of possible water and soil contamination (by agricultural inputs). Probabilistic spatial relations provide information on the layout of spatial objects. Probabilities are stored in a probabilistic database. Probabilistic database is a finite number of complete databases that are assigned with a set of probabilities. Probabilistic data-aware business processes integrate the theory of probabilistic database with business processes modeling methods. This new formalism of business processes helps the experts to model the environmental risks in terms of probabilistic spatial relations.

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21

Tan, Samson, Darryl Weinert, Paul Joseph, and Khalid Moinuddin. "Impact of Technical, Human, and Organizational Risks on Reliability of Fire Safety Systems in High-Rise Residential Buildings—Applications of an Integrated Probabilistic Risk Assessment Model." Applied Sciences 10, no. 24 (December 14, 2020): 8918. http://dx.doi.org/10.3390/app10248918.

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The current paper presents an application of an alternative probabilistic risk assessment methodology that incorporates technical, human, and organizational risks (T-H-O-Risk) using Bayesian network (BN) and system dynamics (SD) modelling. Seven case studies demonstrate the application of this holistic approach to the designs of high-rise residential buildings. An incremental risk approach allows for quantification of the impact of human and organizational errors (HOEs) on different fire safety systems. The active systems considered are sprinklers, building occupant warning systems, smoke detectors, and smoke control systems. The paper presents detailed results from T-H-O-Risk modelling for HOEs and risk variations over time utilizing the SD modelling to compare risk acceptance in the seven case studies located in Australia, New Zealand, Hong Kong, Singapore, and UK. Results indicate that HOEs impact risks in active systems up to ~33%. Large variations are observed in the reliability of active systems due to HOEs over time. SD results indicate that a small behavioral change in ’risk perception’ of a building management team can lead to a very large risk to life variations over time through the self-reinforcing feedback loops. The quantification of difference in expected risk to life due to technical, human, and organizational risks for seven buildings for each of 16 trial designs is a novel aspect of this study. The research is an important contribution to the development of the next generation building codes and risk assessment methods.

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22

Kuzmanić Skelin, Ana, Lea Vojković, Dani Mohović, and Damir Zec. "Weight of Evidence Approach to Maritime Accident Risk Assessment Based on Bayesian Network Classifier." Transactions on Maritime Science 10, no. 2 (October 21, 2021): 330–47. http://dx.doi.org/10.7225/toms.v10.n02.w07.

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Probabilistic maritime accident models based on Bayesian Networks are typically built upon the data available in accident records and the data obtained from human experts knowledge on accident. The drawback of such models is that they do not take explicitly into the account the knowledge on non-accidents as would be required in the probabilistic modelling of rare events. Consequently, these models have difficulties with delivering interpretation of influence of risk factors and providing sufficient confidence in the risk assessment scores. In this work, modelling and risk score interpretation, as two aspects of the probabilistic approach to complex maritime system risk assessment, are addressed. First, the maritime accident modelling is posed as a classification problem and the Bayesian network classifier that discriminates between accident and non-accident is developed which assesses state spaces of influence factors as the input features of the classifier. Maritime accident risk are identified as adversely influencing factors that contribute to the accident. Next, the weight of evidence approach to reasoning with Bayesian network classifier is developed for an objective quantitative estimation of the strength of factor influence, and a weighted strength of evidence is introduced. Qualitative interpretation of strength of evidence for individual accident influencing factor, inspired by Bayes factor, is defined. The efficiency of the developed approach is demonstrated within the context of collision of small passenger vessels and the results of collision risk assessments are given for the environmental settings typical in Croatian nautical tourism. According to the results obtained, recommendations for navigation safety during high density traffic have been distilled.

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23

Rudari, Roberto, Joost Beckers, Silvia De Angeli, Lauro Rossi, and Eva Trasforini. "Impact of modelling scale on probabilistic flood risk assessment: the Malawi case." E3S Web of Conferences 7 (2016): 04015. http://dx.doi.org/10.1051/e3sconf/20160704015.

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24

Lin, Feng, Xinguang Chen, and Wen Chen. "Modelling and control of multi-risk behaviours using probabilistic discrete event systems." International Journal of Automation and Control 8, no. 4 (2014): 295. http://dx.doi.org/10.1504/ijaac.2014.065452.

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25

Duzinkiewicz, Kazimierz, Kazimierz T. Kosmowski, and Miroslaw Kwiesielewicz. "Fuzzy Probabilistic Modelling of Complex Hazardous Plants and Risk Assessment Under Uncertainties." IFAC Proceedings Volumes 30, no. 18 (August 1997): 745–50. http://dx.doi.org/10.1016/s1474-6670(17)42489-x.

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26

Barton, D. N., T. Saloranta, T. H. Bakken, A. Lyche Solheim, J. Moe, J. R. Selvik, and N. Vagstad. "Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive." Water Supply 5, no. 6 (December 1, 2005): 95–104. http://dx.doi.org/10.2166/ws.2005.0054.

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The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

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27

Anderson, Paul D., and Allison L. Yuhas. "Improving Risk Management by Characterizing Reality: A Benefit of Probabilistic Risk Assessment." Human and Ecological Risk Assessment: An International Journal 2, no. 1 (March 1996): 55–58. http://dx.doi.org/10.1080/10807039.1996.10387460.

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28

Polzer, Stanislav, and T. Christian Gasser. "Biomechanical rupture risk assessment of abdominal aortic aneurysms based on a novel probabilistic rupture risk index." Journal of The Royal Society Interface 12, no. 113 (December 2015): 20150852. http://dx.doi.org/10.1098/rsif.2015.0852.

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A rupture risk assessment is critical to the clinical treatment of abdominal aortic aneurysm (AAA) patients. The biomechanical AAA rupture risk assessment quantitatively integrates many known AAA rupture risk factors but the variability of risk predictions due to model input uncertainties remains a challenging limitation. This study derives a probabilistic rupture risk index (PRRI). Specifically, the uncertainties in AAA wall thickness and wall strength were considered, and wall stress was predicted with a state-of-the-art deterministic biomechanical model. The discriminative power of PRRI was tested in a diameter-matched cohort of ruptured ( n = 7) and intact ( n = 7) AAAs and compared to alternative risk assessment methods. Computed PRRI at 1.5 mean arterial pressure was significantly ( p = 0.041) higher in ruptured AAAs (20.21(s.d. 14.15%)) than in intact AAAs (3.71(s.d. 5.77)%). PRRI showed a high sensitivity and specificity (discriminative power of 0.837) to discriminate between ruptured and intact AAA cases. The underlying statistical representation of stochastic data of wall thickness, wall strength and peak wall stress had only negligible effects on PRRI computations. Uncertainties in AAA wall stress predictions, the wide range of reported wall strength and the stochastic nature of failure motivate a probabilistic rupture risk assessment. Advanced AAA biomechanical modelling paired with a probabilistic rupture index definition as known from engineering risk assessment seems to be superior to a purely deterministic approach.

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Vagdatli, Theodora, and Kleopatra Petroutsatou. "Modelling Approaches of Life Cycle Cost–Benefit Analysis of Road Infrastructure: A Critical Review and Future Directions." Buildings 13, no. 1 (December 30, 2022): 94. http://dx.doi.org/10.3390/buildings13010094.

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Cost–benefit analysis (CBA) is considered an effective evaluation method for fostering optimal decision making and ranking of road infrastructures over decades. This paper introduces a comprehensive systematic literature review focusing on CBA’s methodological perspective for identifying its current modelling approaches and scrutinizing their key features and encompassed tools. Fifty-six relevant studies were extracted from Web of Science, Scopus and Google Scholar from 2005 until 2020. Initially, the bibliometric analysis presents an overall illustration of the most significant CBA concepts. The descriptive statistics determined eight distinct modelling categories used for CBA implementation, each encompassing three different modelling approaches for capturing the data risk assessment (deterministic or probabilistic), CBA’s parameters interactive behavior (static or dynamic) and the considered economies (microeconomic or macroeconomic). In-depth content analysis led to the interpretation of the current status of extant models and the identification of three main knowledge gaps: the absence of the CBA’s inputs updating into a probabilistic environment, the deficiency of a dynamic interdependent framework and the necessity of homogenous cost datasets for road projects. Future research directions and a conceptual framework for modelling CBA into a microeconomic, probabilistic and dynamic environment are proposed providing decision-makers with new avenues for more reliable CBA modelling.

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Khan, Md Jamal Uddin, Fabien Durand, Kerry Emanuel, Yann Krien, Laurent Testut, and A. K. M. Saiful Islam. "Storm surge hazard over Bengal delta: a probabilistic–deterministic modelling approach." Natural Hazards and Earth System Sciences 22, no. 7 (July 18, 2022): 2359–79. http://dx.doi.org/10.5194/nhess-22-2359-2022.

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Abstract. Storm-surge-induced coastal inundation constitutes a substantial threat to lives and properties along the vast coastline of the Bengal delta. Some of the deadliest cyclones in history made landfall in the Bengal delta region claiming more than half a million lives over the last five decades. Complex hydrodynamics and observational constraints have hindered the understanding of the risk of storm surge flooding of this low-lying (less than 5 m above mean sea level), densely populated (> 150 million) mega-delta. Here, we generated and analysed a storm surge database derived from a large ensemble of 3600 statistically and physically consistent synthetic storm events and a high-resolution storm surge modelling system. The storm surge modelling system is developed based on a custom high-accuracy regional bathymetry enabling us to estimate the surges with high confidence. From the storm surge dataset, we performed a robust probabilistic estimate of the storm surge extremes. Our ensemble estimate shows that there is a diverse range of water level extremes along the coast and the estuaries of the Bengal delta, with well-defined regional patterns. We confirm that the risk of inland storm surge flooding at a given return period is firmly controlled by the presence of coastal embankments and their height. We also conclude that about 10 % of the coastal population is living under the exposure of a 50-year return period inundation under current climate scenarios. In the face of ongoing climate change, which is likely to worsen the future storm surge hazard, we expect our flood maps to provide relevant information for coastal infrastructure engineering, risk zoning, resource allocation, and future research planning.

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Iqbal, Asif, Shirina Afroze, and Md Mizanur Rahman. "Probabilistic Health Risk Assessment of Vehicular Emissions as an Urban Health Indicator in Dhaka City." Sustainability 11, no. 22 (November 15, 2019): 6427. http://dx.doi.org/10.3390/su11226427.

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Emissions modelling is an important tool for assessing the urban health status of any city, but often the assessments are affected by the uncertainty of the data used for the modelling. Therefore, a Monte Carlo simulation technique was used for a probabilistic emissions modelling of Dhaka City by simulating 20,000 scenarios for the highest and lowest values of traffic volume and speed profiles for each of the major road links. Only nitrogen oxide (NOx) emissions from on-road vehicles were considered, as vehicular sources are major contributors. Each dataset included two peak periods and an offpeak period of the day to cover the diurnal variation within each road link. Using the probability of the magnitude of emissions along with the corresponding health risk, a series of spatial urban health risk severity scenarios was generated for 2018 and 2024, suggesting that transportation and environmental planning is required for urban sustainability.

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Бантюков, Сергій Євгенович, and Світлана Олександрівна Бантюкова. "Logical and probabilistic modelling concerning risk of safe functioning violation of hump yard." Eastern-European Journal of Enterprise Technologies 1, no. 3(67) (February 17, 2014): 30. http://dx.doi.org/10.15587/1729-4061.2014.19896.

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33

Grêt-Regamey, A., and D. Straub. "Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS." Natural Hazards and Earth System Sciences 6, no. 6 (October 26, 2006): 911–26. http://dx.doi.org/10.5194/nhess-6-911-2006.

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Abstract. Avalanche disasters are associated with significant monetary losses. It is thus crucial that avalanche risk assessments are based on a consistent and proper assessment of the uncertainties involved in the modelling of the avalanche run-out zones and the estimations of the damage potential. We link a Bayesian network (BN) to a Geographic Information System (GIS) for avalanche risk assessment in order to facilitate the explicit modelling of all relevant parameters, their causal relations and the involved uncertainties in a spatially explicit manner. The suggested procedure is illustrated for a case study area (Davos, Switzerland) located in the Swiss Alps. We discuss the potential of such a model by comparing the risks estimated using the probabilistic framework to those obtained by a traditional risk assessment procedure. The presented model may serve as a basis for developing a consistent and unified risk assessment approach.

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34

Baird, Sandra J. S., Joshua T. Cohen, John D. Graham, Alexander I. Shlyakhter, and John S. Evans. "Noncancer Risk Assessment: A Probabilistic Alternative to Current Practice." Human and Ecological Risk Assessment: An International Journal 2, no. 1 (March 1996): 79–102. http://dx.doi.org/10.1080/10807039.1996.10387463.

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35

Hussain, Omar, Tharam Dillon, Farookh Khadeer Hussain, and Elizabeth Chang. "Probabilistic assessment of financial risk in e-business associations." Simulation Modelling Practice and Theory 19, no. 2 (February 2011): 704–17. http://dx.doi.org/10.1016/j.simpat.2010.10.007.

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36

Datla, Suresh V., Mikko I. Jyrkama, and Mahesh D. Pandey. "Probabilistic modelling of steam generator tube pitting corrosion." Nuclear Engineering and Design 238, no. 7 (July 2008): 1771–78. http://dx.doi.org/10.1016/j.nucengdes.2008.01.013.

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37

Beranová, Michaela, and Dana Martinovičová. "Modelling of cost-functions under risk and uncertainty." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 3 (2009): 13–20. http://dx.doi.org/10.11118/actaun200957030013.

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In economics, a cost curve is a graph of the costs of production as a function of total quantity produced. In a free market economy, these curves are used by the entities to find the optimal point of production, where they make the highest profits. There exist several different types of cost curves, while each of them is relevant to a different area of economics. In the article, authors are focused on solution of cost-functions modelling, both short-run cost function and long-run cost function under circumstances of risk and uncertainty. Considerations about factors of risk and uncertainty are based on an irrefutable fact that companies are not separate entities taken out of surrounding environment; entities operate in global world where many random factors are influencing the processes in companies while the number of these random factors is ad infinitum. The fact that estimation of cost functions' parameters are realized from past data is the basis of the considerations about planning of future scope of production based on these functions. Especially for the long-run cost functions it is impossible to leave out all the random influences. Their quantification is derived from aposteriori probabilistic approach according to Bayesian Theorem.

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38

Al-Humaidi, H. M. "Probabilistic and fuzzy fault-tree analyses for modelling cave-in accidents." International Journal of Safety and Security Engineering 3, no. 3 (September 30, 2013): 165–73. http://dx.doi.org/10.2495/safe-v3-n3-165-173.

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Twining, J., N. Creighton, S. Hollins, and R. Szymczak. "Probabilistic Risk Assessment and Risk Mapping of Sediment Metals in Sydney Harbour Embayments." Human and Ecological Risk Assessment: An International Journal 14, no. 6 (November 21, 2008): 1202–25. http://dx.doi.org/10.1080/10807030802494493.

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Bayraktarli, Yahya Y., Jack W. Baker, and Michael H. Faber. "Uncertainty treatment in earthquake modelling using Bayesian probabilistic networks." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 5, no. 1 (March 2011): 44–58. http://dx.doi.org/10.1080/17499511003679931.

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Hao, Lu, Li-Zhe Yang, and Jing-Min Gao. "The application of information diffusion technique in probabilistic analysis to grassland biological disasters risk." Ecological Modelling 272 (January 2014): 264–70. http://dx.doi.org/10.1016/j.ecolmodel.2013.10.014.

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Ribeiro, Andreia F. S., Ana Russo, Célia M. Gouveia, Patrícia Páscoa, and Carlos A. L. Pires. "Probabilistic modelling of the dependence between rainfed crops and drought hazard." Natural Hazards and Earth System Sciences 19, no. 12 (December 11, 2019): 2795–809. http://dx.doi.org/10.5194/nhess-19-2795-2019.

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Abstract. Extreme weather events, such as droughts, have been increasingly affecting the agricultural sector, causing several socio-economic consequences. The growing economy requires improved assessments of drought-related impacts in agriculture, particularly under a climate that is getting drier and warmer. This work proposes a probabilistic model that is intended to contribute to the agricultural drought risk management in rainfed cropping systems. Our methodology is based on a bivariate copula approach using elliptical and Archimedean copulas, the application of which is quite recent in agrometeorological studies. In this work we use copulas to model joint probability distributions describing the amount of dependence between drought conditions and crop yield anomalies. Afterwards, we use the established copula models to simulate pairs of yield anomalies and drought hazard, preserving their dependence structure to further estimate the probability of crop loss. In the first step, we analyse the probability of crop loss without distinguishing the class of drought, and in the second step we compare the probability of crop loss under drought and non-drought conditions. The results indicate that, in general, Archimedean copulas provide the best statistical fits of the joint probability distributions, suggesting a dependence among extreme values of rainfed cereal yield anomalies and drought indicators. Moreover, the estimated conditional probabilities suggest that when drought conditions are below moderate thresholds, the risk of crop loss increases between 32.53 % (cluster 1) and 32.6 % (cluster 2) in the case of wheat and between 31.63 % (cluster 2) and 55.55 % (cluster 2) in the case of barley. From an operational point of view, the results aim to contribute to the decision-making process in agricultural practices.

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Sun, Tian Yin, Gulliver Conroy, Erica Donner, Konrad Hungerbühler, Enzo Lombi, and Bernd Nowack. "Probabilistic modelling of engineered nanomaterial emissions to the environment: a spatio-temporal approach." Environmental Science: Nano 2, no. 4 (2015): 340–51. http://dx.doi.org/10.1039/c5en00004a.

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Kropf, Chahan M., Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch. "Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0." Geoscientific Model Development 15, no. 18 (September 23, 2022): 7177–201. http://dx.doi.org/10.5194/gmd-15-7177-2022.

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Abstract. Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here, we present a new feature of the climate-risk modelling platform CLIMADA (CLIMate ADAptation), which allows us to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision-makers with a fact base to understand the impact of weather and climate on their economies, communities, and ecosystems, including the appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analysis will enhance transparency and intercomparison of studies among climate-risk modellers and help focus future research. For decision-makers and other users of climate-risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualize risk assessment and options appraisal, and might be used to inform the development of storylines and climate adaptation narratives.

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Johnston, John J., and Jamie L. Snow. "Population-Based Fish Consumption Survey and Probabilistic Methylmercury Risk Assessment." Human and Ecological Risk Assessment: An International Journal 13, no. 6 (November 9, 2007): 1214–27. http://dx.doi.org/10.1080/10807030701655764.

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Guo, Jun, Sijian Zhao, and Chongfu Huang. "Valid historical data for probabilistic risk analysis in natural disasters." Human and Ecological Risk Assessment: An International Journal 23, no. 3 (March 31, 2017): 474–93. http://dx.doi.org/10.1080/10807039.2016.1247253.

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Khan, Faisal I., and Tahir Husain. "Risk Assessment and Safety Evaluation Using Probabilistic Fault Tree Analysis." Human and Ecological Risk Assessment: An International Journal 7, no. 7 (December 2001): 1909–27. http://dx.doi.org/10.1080/20018091095483.

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Fakhruddin, Bapon, Karunakar Kintada, and Quamrul Hassan. "Understanding hazards: Probabilistic cyclone modelling for disaster risk to the Eastern Coast in Bangladesh." Progress in Disaster Science 13 (January 2022): 100216. http://dx.doi.org/10.1016/j.pdisas.2022.100216.

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Ruprich, J., I. Rehurkova, P. E. Boon, K. Svensson, S. Moussavian, H. Van der Voet, S. Bosgra, J. D. Van Klaveren, and L. Busk. "Probabilistic modelling of exposure doses and implications for health risk characterization: Glycoalkaloids from potatoes." Food and Chemical Toxicology 47, no. 12 (December 2009): 2899–905. http://dx.doi.org/10.1016/j.fct.2009.03.008.

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Baklanov, A. A., and A. G. Mahura. "Assessment of possible airborne impact from nuclear risk sites – Part I: methodology for probabilistic atmospheric studies." Atmospheric Chemistry and Physics Discussions 3, no. 5 (October 22, 2003): 5289–317. http://dx.doi.org/10.5194/acpd-3-5289-2003.

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Abstract. The main purpose of this study is to develop a methodology for a multidisciplinary nuclear risk and vulnerability assessment, and to test this methodology through estimation of a nuclear risk to population in the Northern European countries in case of a severe accident at the nuclear risk sites. For assessment of the probabilistic risk and vulnerability, a combination of social-geophysical factors and probabilities are considered. The main focus of this paper is the description of methodology for evaluation of the atmospheric transport of radioactive releases from the risk site regions. The suggested methodology is given from the probabilistic point of view. The main questions stated are: What are probabilities and times for radionuclide atmospheric transport to different neighbouring countries and territories in case of the hypothetical accidental release at the nuclear risk site? Which geographical territories or countries are at the highest risk from the hypothetical accidental releases? To answer this question we suggest applying the following research tools for probabilistic atmospheric studies. First, it is atmospheric modelling to calculate multiyear forward trajectories originated over the sites. Second, it is statistical analysis tools to explore temporal and spatial structure of calculated trajectories in order to evaluate different probabilistic impact indicators: atmospheric transport pathways, airflow, fast transport, typical transport time, maximum possible impact zone, maximum reaching distance, etc. These indicators are applicable for further GIS-analysis and integration to estimate regional risk and vulnerability in case of accidental releases at the risk sites and for planning the emergency response and preparedness systems.

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