An integrated model for simulating and diagnosing the water quality based on the system dynamics and Bayesian network

Wang, Gengzhe; Wang, Shuo; Kang, Qiao; Duan, Haiyan; Wang, Xian’en

doi:10.2166/wst.2016.442

Cited by 10 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Existing studies considered the combination of a probabilistic evaluation performed by BN with one deterministic approach, like SDMs or ABM (Bertone et al, 2016a;Phan et al, 2016;Wang et al, 2016;Pope and Gimblett, 2015;Kocabas and Dragicevic, 2013). Advantages of this "hybridization" include the capacity of dealing with a high degree of uncertainty, the use of feedback loops and the integration of quantitative and qualitative data.…”

Section: Multi-risk Criteria Fulfilmentmentioning

confidence: 99%

“…Moreover, Wang et al (2016), Pope and Gimblett (2015) and Kocabas and Dragicevic (2013) performed a spatial analysis together with discrete temporal representation, looking at changes of systems variables and creating output maps. In addition to that, Pope and Gimblett (2015) also extended the analysis to future scenarios of climate change, assessing water scarcity impacts across different sectors (i.e.…”

Section: Multi-risk Criteria Fulfilmentmentioning

confidence: 99%

See 1 more Smart Citation

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Terzi

Torresan

Schneiderbauer

et al. 2019

Journal of Environmental Management

154

View full text Add to dashboard Cite

Climate change has already led to a wide range of impacts on our society, the economy and the environment. According to future scenarios, mountain regions are highly vulnerable to climate impacts, including changes in the water cycle (e.g. rainfall extremes, melting of glaciers, river runoff), loss of biodiversity and ecosystems services, damages to local economy (drinking water supply, hydropower generation, agricultural suitability) and human safety (risks of natural hazards). This is due to their exposure to recent climate warming (e.g. temperature regime changes, thawing of permafrost) and the high degree of specialization of both natural and human systems (e.g. mountain species, valley population density, tourism-based economy). These characteristics call for the application of risk assessment methodologies able to describe the complex interactions among multiple hazards, biophysical and socioeconomic systems, towards climate change adaptation. Current approaches used to assess climate change risks often address individual risks separately and do not fulfil a comprehensive representation of cumulative effects associated to different hazards (i.e. compound events). Moreover, pioneering multi-layer single risk assessment (i.e. overlapping of single-risk assessments addressing different hazards) is still widely used, causing misleading evaluations of multi-risk processes. This raises key questions about the distinctive features of multi-risk assessments and the available tools and methods to address them. Here we present a review of five cutting-edge modelling approaches (Bayesian networks, agent-based models, system dynamic models, event and fault trees, and hybrid models), exploring their potential applications for multi-risk assessment and climate change adaptation in mountain regions. The comparative analysis sheds light on advantages and limitations of each approach, providing a roadmap for methodological and technical implementation of multi-risk assessment according to distinguished criteria (e.g. spatial and temporal dynamics, uncertainty management, cross-sectoral assessment, adaptation measures integration, data required and level of complexity). The results show limited applications of the selected methodologies in addressing the climate and risks challenge in mountain environments. In particular, system dynamic and hybrid models demonstrate higher potential for further applications to represent climate change effects on multi-risk processes for an effective implementation of climate adaptation strategies.

show abstract

Section: Multi-risk Criteria Fulfilmentmentioning

confidence: 99%

Section: Multi-risk Criteria Fulfilmentmentioning

confidence: 99%

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Terzi

Torresan

Schneiderbauer

et al. 2019

Journal of Environmental Management

154

View full text Add to dashboard Cite

show abstract

“…', through conducting trade-off analysis to find out the most optimal management options with/without exploring different scenarios (e.g., [17,137]). Only 27% of reviewed studies developed BNs for predictive modelling only, meaning that these studies just focused on understanding the systems without exploring any scenarios or management measures (e.g., [36,60,141]).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

Phan

Smart

Stewart‐Koster

et al. 2019

Water

View full text Add to dashboard Cite

Bayesian networks (BNs) are widely implemented as graphical decision support tools which use probability inferences to generate “what if?” and “which is best?” analyses of potential management options for water resource management, under climate change and socio-economic stressors. This paper presents a systematic quantitative literature review of applications of BNs for decision support in water resource management. The review quantifies to what extent different types of data (quantitative and/or qualitative) are used, to what extent optimization-based and/or scenario-based approaches are adopted for decision support, and to what extent different categories of adaptation measures are evaluated. Most reviewed publications applied scenario-based approaches (68%) to evaluate the performance of management measures, whilst relatively few studies (18%) applied optimization-based approaches to optimize management measures. Institutional and social measures (62%) were mostly applied to the management of water-related concerns, followed by technological and engineered measures (47%), and ecosystem-based measures (37%). There was no significant difference in the use of quantitative and/or qualitative data across different decision support approaches (p = 0.54), or in the evaluation of different categories of management measures (p = 0.25). However, there was significant dependence (p = 0.076) between the types of management measure(s) evaluated, and the decision support approaches used for that evaluation. The potential and limitations of BN applications as decision support systems are discussed along with solutions and recommendations, thereby further facilitating the application of this promising decision support tool for future research priorities and challenges surrounding uncertain and complex water resource systems driven by multiple interactions amongst climatic and non-climatic changes.

show abstract

“…They have complementary advantages and disadvantages, and thus it has been suggested to use both models to complement each other for a comprehensive water management assessment (Sušnik et al 2013). However, to the authors' knowledge, confirmed by recent reviews (Phan et al 2016b), there have been very limited attempts to combine these two modelling approaches (Phan et al 2016a;Wang et al 2016), in particular no attempts of using BN knowledge to develop a SD that can deal with missing data and uncertainty.…”

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

View full text Add to dashboard Cite

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.

show abstract

An integrated model for simulating and diagnosing the water quality based on the system dynamics and Bayesian network

Cited by 10 publications

References 26 publications

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

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

Contact Info

Product

Resources

About