“…BN, also known as Bayesian Belief Network (BBN), composed by graphical structure and Bayes' theorem, constitutes a widely accepted formalism for representing uncertain knowledge (subjective or objective) and for efficiently reasoning with it (Guarnieri et al 2015;Abebe et al 2018). A complete BN model, as presented in Equation (1), contains a qualitative component and a quantitative one (Chen et al 2019). The qualitative component of BN is a directed acyclic graph, where nodes and directed links signify system variables and their causal dependencies.…”
Section: Bn Model and Gis Descriptionmentioning
confidence: 99%
“…The Bayesian network (BN) model, a representative way of knowledge representation and reasoning, has many advantages, such as identifying relevant variables and their relationships in the form of graphical representation, using prior knowledge that can be learned from actual data to infer and reason (Couasnon et al 2018). Therefore, BN model has become a significant tool for dealing with complex problem, which was widely used in intelligent reasoning (Napolitano et al 2010), prediction (Poelhekke et al 2016), risk analysis (Chen et al 2019), and so on. GIS (geographic information system) is an appropriate tool for processing data with attributes for deriving regional indicators on flood disaster risk (Mahmoud and Gan 2018).…”
With global climate change, cities face the challenge of increasing flood disaster caused by heavy rainfall, and the prediction and assessment of flood disaster risk is a crucial step towards risk mitigation and adaptation planning. In this study, a method combining Bayesian network (BN) model and geographic information system (GIS), which can capture the potential relationships between factors impacting flood disaster and has capacity of quantifying uncertainty and utilizing both data and knowledgebased sources, was proposed to assess flood disaster risk. The proposed methodology was applied in a case study to assess flood disaster risk and to diagnose the reason for flood disaster in Zhengzhou City, and the results were validated by comparing with actual situation. The results show that that the relative error of very-low, low, moderate, high and very-high risk predicted by the proposed model is 12.57%, 13.21%, 2.23%, 19.63% and 21.65%, respectively, which demonstrates the discriminative power of the established model. Based on the spatial distribution of different risk levels, it can be recognized that the flood disaster risk in Zhengzhou City is decreasing from the middle to the surroundings. The results provide some basis for the field control and management of urban flood disaster.
“…BN, also known as Bayesian Belief Network (BBN), composed by graphical structure and Bayes' theorem, constitutes a widely accepted formalism for representing uncertain knowledge (subjective or objective) and for efficiently reasoning with it (Guarnieri et al 2015;Abebe et al 2018). A complete BN model, as presented in Equation (1), contains a qualitative component and a quantitative one (Chen et al 2019). The qualitative component of BN is a directed acyclic graph, where nodes and directed links signify system variables and their causal dependencies.…”
Section: Bn Model and Gis Descriptionmentioning
confidence: 99%
“…The Bayesian network (BN) model, a representative way of knowledge representation and reasoning, has many advantages, such as identifying relevant variables and their relationships in the form of graphical representation, using prior knowledge that can be learned from actual data to infer and reason (Couasnon et al 2018). Therefore, BN model has become a significant tool for dealing with complex problem, which was widely used in intelligent reasoning (Napolitano et al 2010), prediction (Poelhekke et al 2016), risk analysis (Chen et al 2019), and so on. GIS (geographic information system) is an appropriate tool for processing data with attributes for deriving regional indicators on flood disaster risk (Mahmoud and Gan 2018).…”
With global climate change, cities face the challenge of increasing flood disaster caused by heavy rainfall, and the prediction and assessment of flood disaster risk is a crucial step towards risk mitigation and adaptation planning. In this study, a method combining Bayesian network (BN) model and geographic information system (GIS), which can capture the potential relationships between factors impacting flood disaster and has capacity of quantifying uncertainty and utilizing both data and knowledgebased sources, was proposed to assess flood disaster risk. The proposed methodology was applied in a case study to assess flood disaster risk and to diagnose the reason for flood disaster in Zhengzhou City, and the results were validated by comparing with actual situation. The results show that that the relative error of very-low, low, moderate, high and very-high risk predicted by the proposed model is 12.57%, 13.21%, 2.23%, 19.63% and 21.65%, respectively, which demonstrates the discriminative power of the established model. Based on the spatial distribution of different risk levels, it can be recognized that the flood disaster risk in Zhengzhou City is decreasing from the middle to the surroundings. The results provide some basis for the field control and management of urban flood disaster.
“…Hence, this confirms the results of this study. The methodological framework presented in this study is consistent with [ 69 ], in which researchers used DBN to model risk assessment of flood control operations. Fig.…”
Section: Resultsmentioning
confidence: 69%
“…However, the risk factor of transportation should be based on vehicles to collect the biomass from fields and its transportation to bioconversion sites. The methodological framework presented is consistent with [ 69 ], in which, using experts’ opinions, researchers developed a DBN model to perform a risk assessment of flood control operations. Future work is recommended to expand the analysis by including the risk factors associated with bioconversion and biofuel delivery to customers.…”
The novel coronavirus (COVID-19) is highly detrimental, and its death distribution peculiarity has severely affected people’s health and the operations of businesses. COVID-19 has wholly undermined the global economy, including significant damage to the ever-emerging biomass supply chain; its sustainability is disintegrating due to the coronavirus. The biomass supply chain must be sustainable and robust enough to adapt to the evolving and fluctuating risks of the market due to the coronavirus. However, no such study has been performed so far. To address this issue, investigating how COVID-19 influences a biomass supply chain is vital. This paper presents a dynamic risk assessment methodological framework to model biomass supply chain risks due to COVID-19. Using a dynamic Bayesian network (DBN) formalism, the impacts of COVID-19 on the performance of biomass supply chain risks were studied. The proposed model was applied to the biomass supply chain of a U.S.-based General Biodiesel company in Washington, USA. The case study results show that it would take one year to recover from the maximum damage to the biomass supply chain due to COVID-19, while full recovery would require five years. Results indicate that biomass feedstock gate availability (FGA) is 2% due to pandemic and lockdown conditions. Due to the availability of vaccination and gradual business reopenings, this availability increases to 92% in the second year. Results also indicate that the price of fossil-based fuel will gradually increase after one year of the pandemic; however, the market prices of fossil-based fuel will not revert to pre-coronavirus conditions even after nine years. K-fold cross-validation is used to validate DBN. Results of validation indicate a model accuracy of 95%. It is concluded that pandemic has caused risks to the sustainability of biomass feedstock, and the current study can help develop risk mitigation strategies.
“…With continuously growing populations, water resources are becoming more and more important for urbanization and agricultural intensification, especially for developing countries [1][2][3]. In the process of water resource planning, streamflow forecasting plays a key role in hydrological risk assessment, reservoir operations, drought/flood prevention, and water resource allocation [4][5][6]. More importantly, the management efficiency of water resource systems mainly depends on the reliability and accuracy of hydrological prediction.…”
In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices.
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