Risk analysis of emergent water pollution accidents based on a Bayesian Network

Tang, Changyuan; Yi, Yang; Zhang, Yang; Sun, Jie

doi:10.1016/j.jenvman.2015.09.024

Cited by 48 publications

(29 citation statements)

References 25 publications

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“…The Bayesian network method is based on the Bayesian formula. It can be used to express the relationship between the prior probability and conditional probability of a given node and also to derive the incidence probability of a specific event related to that node [27].…”

Section: Bayesian Network Approachmentioning

confidence: 99%

Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Namazian

Yakhchali

Yousefi

et al. 2019

IJERPH

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In this study, Monte Carlo simulation and Bayesian network methods are combined to present a structure for assessing the aggregated impact of risks on the completion time of a construction project. Construction projects often encounter different risks which affect and prevent their desired completion at the predicted time and budget. The probability of construction project success is increased in the case of controlling influential risks. On the other hand, interactions among risks lead to the increase of aggregated impact of risks. This fact requires paying attention to assessment and management of project aggregated risk before and during the implementation phase. The developed structure of this article considers the interactions among risks to provide an indicator for estimating the effects of risks, so that the shortage of extant models including the lack of attention to estimate the aggregated impact caused by risks and the intensifying impacts can be evaluated. Moreover, the introduced structure is implemented in an industrial case study in order to validate the model, cover the functional aspect of the problem, and explain the procedure of structure implementation in detail.

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Section: Bayesian Network Approachmentioning

confidence: 99%

Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Namazian

Yakhchali

Yousefi

et al. 2019

IJERPH

View full text Add to dashboard Cite

show abstract

“…The disadvantage is that discretisation inevitably leads to a coarser representation of the system. BN have been proved to be accurate modelling tools for water quality predictions (Avila et al 2018), and they have been applied for risk assessment in relation to water pollution issues (Tang et al 2016). They have also been applied to model vulnerability and uncertainty in emergency water management situations (Pagano et al 2018), or to rank different water management options (Said 2006).…”

Section: Extreme Events and Water Quality: Modelling Challengesmentioning

confidence: 99%

Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

et al. 2018

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A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

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“…The MRP stretches across the Hebei Province from south to north, and 697 bridges have been built to recover transportation along the canal [13]. These bridges had potential pollution risk for transferred water [3].…”

Section: Water Quality Predictionmentioning

confidence: 99%

“…Heavy metal pollution has been attracting more attentions [36]. Road transport of raw materials made an increased emergent pollution risks for the MRP under influences of weather, road, and environmental factors [3]. To forecast water quality changes and provide suggestions for water resources regulation, typical nonconservative contaminant VOP and conservation contaminant mercury (Hg) were chosen as the emergent pollutants for the MRP in this study.…”

Section: Water Quality Predictionmentioning

confidence: 99%

“…Open channel is the main way to transfer water in south of the Beijing territory for this project. Meanwhile, transferred water in the open channel has a high risk of water pollution resulting from atmosphere deposition, groundwater pollution, discharged polluted water, and sudden pollution accidents [3]. Therefore, protecting water quality is still an emphasis on water transfer and water resources management [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A One‐Dimensional Hydrodynamic and Water Quality Model for a Water Transfer Project with Multihydraulic Structures

Tang

Zhang

et al. 2017

Mathematical Problems in Engineering

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The long Middle Route of the South to North Water Transfer Project is composed of complex hydraulic structures (aqueduct, tunnel, control gate, diversion, culvert, and diverted siphon), which generate complex flow patterns. It is vital to simulate the flow patterns through hydraulic structures, but it is a challenging work to protect water quality and maintain continuous water transfer. A one-dimensional hydrodynamic and water quality model was built to understand the flow and pollutant movement in this project. Preissmann four-point partial-node implicit scheme was used to solve the governing equations in this study. Water flow and pollutant movement were appropriately simulated and the results indicated that this water quality model was comparable to MIKE 11 and had a good performance and accuracy. Simulation accuracy and model uncertainty were analyzed. Based on the validated water quality model, six pollution scenarios ( 1 = 10 m 3 /s, 2 = 30 m 3 /s, and 3 = 60 m 3 /s for volatile phenol (VOP) and contaminant mercury (Hg)) were simulated for the MRP. Emergent pollution accidents were forecasted and changes of water quality were analyzed according to the simulations results, which helped to guarantee continuously transferring water for a large water transfer project.

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Risk analysis of emergent water pollution accidents based on a Bayesian Network

Cited by 48 publications

References 25 publications

Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk

Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

A One‐Dimensional Hydrodynamic and Water Quality Model for a Water Transfer Project with Multihydraulic Structures

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