The Experts below are selected from a list of 5967 Experts worldwide ranked by ideXlab platform
L C Rietveld - One of the best experts on this subject based on the ideXlab platform.
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a Bayesian Belief Network model to link sanitary inspection data to drinking water quality in a medium resource setting in rural indonesia
Scientific Reports, 2020Co-Authors: D Daniel, Widya Prihesti Iswarani, Saket Pande, L C RietveldAbstract:Assessing water quality and identifying the potential source of contamination, by Sanitary inspections (SI), are essential to improve household drinking water quality. However, no study link the water quality at a point of use (POU), household level or point of collection (POC), and associated SI data in a medium resource setting using a Bayesian Belief Network (BBN) model. We collected water samples and applied an adapted SI at 328 POU and 265 related POC from a rural area in East Sumba, Indonesia. Fecal contamination was detected in 24.4 and 17.7% of 1 ml POC and POU samples, respectively. The BBN model showed that the effect of holistic-combined interventions to improve the water quality were larger compared to individual intervention. The water quality at the POU was strongly related to the water quality at the POC and the effect of household water treatment to improve the water quality was more prominent in the context of better sanitation and hygiene conditions. In addition, it was concluded that the inclusion of extra "external" variable (fullness level of water at storage), besides the standard SI variables, could improve the model's performance in predicting the water quality at POU. Finally, the BBN approach proved to be able to illustrate the interdependencies between variables and to simulate the effect of the individual and combination of variables on the water quality.
Solomon Tesfamariam - One of the best experts on this subject based on the ideXlab platform.
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quantifying restoration time of power and telecommunication lifelines after earthquakes using Bayesian Belief Network model
Reliability Engineering & System Safety, 2021Co-Authors: Melissa De Iuliis, Omar Kammouh, Gian Paolo Cimellaro, Solomon TesfamariamAbstract:Abstract Natural and human-made disasters can disrupt infrastructures even if they are designed to be hazard resistant. While the occurrence of hazards can only be predicted to some extent, their impact can be managed by increasing the emergency response and reducing the vulnerability of infrastructure. In the context of risk management, the ability of infrastructure to withstand damage and re-establish their initial condition has recently gained prominence. Several resilience strategies have been investigated by numerous scholars to reduce disaster risk and evaluate the recovery time following disastrous events. A key parameter to quantify the seismic resilience of infrastructures is the Downtime (DT). Generally, DT assessment is challenging due to the parameters involved in the process. Such parameters are highly uncertain and therefore cannot be treated in a deterministic manner. This paper proposes a Bayesian Network (BN) probabilistic approach to evaluate the DT of selected infrastructure types following earthquakes. To demonstrate the applicability of the methodology, three scenarios are performed. Results show that the methodology is capable of providing good estimates of infrastructure DT despite the uncertainty of the parameters. The methodology can be used to effectively support decision-makers in managing and minimizing the impacts of earthquakes in immediate post-event applications as well as to promptly recover damaged infrastructure.
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underground sewer Networks renewal complexity assessment and trenchless technology a Bayesian Belief Network and gis framework
Journal of Pipeline Systems Engineering and Practice, 2020Co-Authors: Yekenalem Abebe, Solomon TesfamariamAbstract:AbstractSignificant investment is required to upgrade deteriorating underground sewer Networks. Sewer failure and the subsequent rehabilitation process can have economic, social, and environmental ...
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seismic retrofit screening of existing highway bridges with consideration of chloride induced deterioration a Bayesian Belief Network model
Frontiers in Built Environment, 2018Co-Authors: Solomon Tesfamariam, Emilio Bastidasarteaga, Z LounisAbstract:Vulnerability of seismically deficient bridges, coupled with their ageing and deterioration, pose significant threat to safety, integrity and functionality of the highway Network that could result in significant risks to public safety, traffic disruption, and socio-economic impacts. Given the limited funds available for bridge retrofit, there is a need for an effective management strategy that will enable engineers to identify and prioritise the high-risk bridges for detailed seismic evaluation and retrofit. A practical risk-based preliminary seismic screening technique is proposed in this paper that enables to develop a ranking or prioritization scheme for seismically-deficient bridges. The complex interactions between seismic hazard, bridge vulnerability and consequences of failure are handled in a hierarchical manner. A Bayesian Belief Network based modelling technique is used to aggregate through the hierarchy and generate risk indices by accounting for chloride-induced corrosion deterioration mechanisms. The efficacy of the proposed method is illustrated on two existing bridges that are assumed to be located in high seismic zones and designed under different standards concerning their structural safety under seismic loads and durability performance.
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assessing urban areas vulnerability to pluvial flooding using gis applications and Bayesian Belief Network model
Journal of Cleaner Production, 2018Co-Authors: Yekenalem Abebe, Golam Kabir, Solomon TesfamariamAbstract:Abstract Expected increases in intensity and frequency of rainfall extremes due to climate change, and increased paving and loss of water storage space in urban areas is making cities more susceptible to pluvial flooding. Evaluating the flood vulnerability of urban areas is a crucial step towards risk mitigation and adaptation planning. In this study, a coupled Geographic Information System and Bayesian Belief Network based flood vulnerability assessment model is proposed. The methodology can quantify uncertainty and capture the casual nexus between pluvial flood influencing factors. The model is applied in a case study to diagnose the reason behind the variations in the number of reported basement flooding in different parts of the City of Toronto and to predict Flood Vulnerability Index (FVI). The predicted FVI is validated by comparing the results with the number of approved basement flood subsidy protection program applications. The case study result shows that areas located near downtown Toronto have high FVI and most of the city has medium to low FVI.
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prediction of pipe failure by considering time dependent factors dynamic Bayesian Belief Network model
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A: Civil Engineering, 2017Co-Authors: Gizachew Demissie, Solomon Tesfamariam, Rehan SadiqAbstract:AbstractThe water supply system (WSS) is a lifeline of the modern city. Transmission and distribution pipes, spatially distributed components of the WSS, are most often vulnerable to failure (leaka...
D Daniel - One of the best experts on this subject based on the ideXlab platform.
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a Bayesian Belief Network model to link sanitary inspection data to drinking water quality in a medium resource setting in rural indonesia
Scientific Reports, 2020Co-Authors: D Daniel, Widya Prihesti Iswarani, Saket Pande, L C RietveldAbstract:Assessing water quality and identifying the potential source of contamination, by Sanitary inspections (SI), are essential to improve household drinking water quality. However, no study link the water quality at a point of use (POU), household level or point of collection (POC), and associated SI data in a medium resource setting using a Bayesian Belief Network (BBN) model. We collected water samples and applied an adapted SI at 328 POU and 265 related POC from a rural area in East Sumba, Indonesia. Fecal contamination was detected in 24.4 and 17.7% of 1 ml POC and POU samples, respectively. The BBN model showed that the effect of holistic-combined interventions to improve the water quality were larger compared to individual intervention. The water quality at the POU was strongly related to the water quality at the POC and the effect of household water treatment to improve the water quality was more prominent in the context of better sanitation and hygiene conditions. In addition, it was concluded that the inclusion of extra "external" variable (fullness level of water at storage), besides the standard SI variables, could improve the model's performance in predicting the water quality at POU. Finally, the BBN approach proved to be able to illustrate the interdependencies between variables and to simulate the effect of the individual and combination of variables on the water quality.
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A hierarchical Bayesian Belief Network model of household water treatment behaviour in a suburban area: A case study of Palu-Indonesia.
'Public Library of Science (PLoS)', 2020Co-Authors: D Daniel, Mita Sirait, Saket PandeAbstract:Understanding the determinants of household water treatment (HWT) behavior in developing countries is important to increase the rate of its regular use so that households can have safe water at home. This is especially so when the quality of the water source is not reliable. We present a hierarchical Bayesian Belief Network (BBN) model supported by statistical analysis to explore the influence of household's socio-economic characteristics (SECs) on the HWT behavior via household's psychological factors. The model uses eight SECs, such as mother's and father's education, wealth, and religion, and five RANAS psychological factors, i.e., risk, attitude, norms, ability, and self-regulation to analyse HWT behavior in a suburban area in Palu, Indonesia. Structured household interviews were conducted among 202 households. We found that mother's education is the most important SEC that influences the regular use of HWT. An educated mother has more positive attitude towards HWT and is more confident in her ability to perform HWT. Moreover, self-regulation, especially the attempt to deal with any barrier that hinders HWT practice, is the most important psychological factor that can change irregular HWT users to regular HWT users. Hence, this paper recommends to HWT-program implementers to identify potential barriers and discuss potential solutions with the target group in order to increase the probability of the target group being a regular HWT user
Dragan Savic - One of the best experts on this subject based on the ideXlab platform.
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an evolutionary Bayesian Belief Network methodology for participatory decision making under uncertainty an application to groundwater management
Integrated Environmental Assessment and Management, 2012Co-Authors: Raziyeh Farmani, Hans Jorgen Henriksen, Dragan Savic, David ButlerAbstract:An integrated participatory approach based on Bayesian Belief Network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives. Integr Environ Assess Manag 2012; 8: 456–461. © SETAC
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an evolutionary Bayesian Belief Network methodology for optimum management of groundwater contamination
Environmental Modelling and Software, 2009Co-Authors: Raziyeh Farmani, Hans Jorgen Henriksen, Dragan SavicAbstract:An integrated methodology, based on Bayesian Belief Network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian Belief Network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the Network could be difficult, the proposed methodology can be used in validation of the Network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian Belief Network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian Belief Network. It is possible then to calculate the probabilities for all nodes in the Network (Belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian Belief Networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.
Raziyeh Farmani - One of the best experts on this subject based on the ideXlab platform.
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an evolutionary Bayesian Belief Network methodology for participatory decision making under uncertainty an application to groundwater management
Integrated Environmental Assessment and Management, 2012Co-Authors: Raziyeh Farmani, Hans Jorgen Henriksen, Dragan Savic, David ButlerAbstract:An integrated participatory approach based on Bayesian Belief Network (BBN) and evolutionary multiobjective optimization is proposed as an efficient decision-making tool in complex management problems. The proposed methodology incorporates all the available evidence and conflicting objectives to evaluate implications of alternative actions in the decision-making process and suggests best decision pathways under uncertainty. A BBN provides a framework within which the contributions of stakeholders can be taken into account. It allows a range of different factors and their probabilistic relationship to be considered simultaneously. It takes into account uncertainty by assigning probability to those variables whose states are not certain. The integration of BBN with evolutionary multiobjective optimization allows the analysis of tradeoff between different objectives and incorporation and acknowledgement of a broader set of decision goals into the search and decision-making process. The proposed methodology can be used as a decision support tool to model decision-making processes for complex problems. It deals with uncertainties in decision making pertaining to human behavior and checks for consistency of the developed BBN structure and the parameters of the probabilistic relationship by uncovering discrepancies in the decision analysis process (e.g., bias in completeness or redundancy of the model based on a utility function). It generates a set of efficient management options (appropriate combinations of interventions) that balances conflicting objectives. The effectiveness of the proposed methodology is discussed through application to a real case study. It is shown that it successfully identifies any inconsistencies in the developed BBN models and generates large numbers of management options that achieve an optimal tradeoff between different objectives. Integr Environ Assess Manag 2012; 8: 456–461. © SETAC
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an evolutionary Bayesian Belief Network methodology for optimum management of groundwater contamination
Environmental Modelling and Software, 2009Co-Authors: Raziyeh Farmani, Hans Jorgen Henriksen, Dragan SavicAbstract:An integrated methodology, based on Bayesian Belief Network (BBN) and evolutionary multi-objective optimization (EMO), is proposed for combining available evidence to help water managers evaluate implications, including costs and benefits of alternative actions, and suggest best decision pathways under uncertainty. A Bayesian Belief Network is a probabilistic graphical model that represents a set of variables and their probabilistic relationships, which also captures historical information about these dependencies. In complex applications where the task of defining the Network could be difficult, the proposed methodology can be used in validation of the Network structure and the parameters of the probabilistic relationship. Furthermore, in decision problems where it is difficult to choose appropriate combinations of interventions, the states of key variables under the full range of management options cannot be analyzed using a Bayesian Belief Network alone as a decision support tool. The proposed optimization method is used to deal with complexity in learning about actions and probabilities and also to perform inference. The optimization algorithm generates the state variable values which are fed into the Bayesian Belief Network. It is possible then to calculate the probabilities for all nodes in the Network (Belief propagation). Once the probabilities of all the linked nodes have been updated, the objective function values are returned to the optimization tool and the process is repeated. The proposed integrated methodology can help in dealing with uncertainties in decision making pertaining to human behavior. It also eliminates the shortcoming of Bayesian Belief Networks in introducing boundary constraints on probability of state values of the variables. The effectiveness of the proposed methodology is examined in optimum management of groundwater contamination risks for a well field capture zone outside Copenhagen city.
