A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)

Joo, Hongjun; Choi, Changhyun; Kim, Jung-Wook; Kim, Deokhwan; Kim, Soojun; Kim, Hung Soo

doi:10.3390/su11133733

Cited by 25 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The evaluation factors considered in this study are related to the danger of flooding by sub-basin and it is important to clarify that the factors are decided based on the results of previous studies such as [1,15,24,25] and expert judgment. Summarizing, the parameters and sub-parameters that affect the flooding risk of road networks and that are considered in this study are summarized in Figure 2.…”

Section: Factors Affecting the Flood Risk Of Road Infrastructuresmentioning

confidence: 99%

Flood risk assessment for road infrastructures using Bayesian networks: case study of Santarem - Portugal

Arango

Santamaría

Nogal

et al. 2022

APP

View full text Add to dashboard Cite

Assessing flood risks on road infrastructures is critical for the definition of mitigation strategies and adaptation processes. Some efforts have been made to conduct a regional flood risk assessment to support the decision-making process of exposed areas. However, these approaches focus on the physical damage of civil infrastructures without considering indirect impacts resulting from social aspects or traffic delays due to the functionality loss of transportation infrastructures. Moreover, existing methodologies do not include a proper assessment of the uncertainties involved in the risk quantification. This work aims to provide a consistent quantitative flood risk estimation and influence factor modelling for road infrastructures. To this end, a Flood Risk Factor (FRF) is computed as a function of hazard, vulnerability, and infrastructure importance factors. A Bayesian Network (BN) is constructed for considering the interdependencies among the selected input factors, as well as accounting for the uncertainties involved in the modelling process. The proposed approach allows weighting the relevant factors differently to compute the FRF and improves the understanding of the causal relations between them. The suggested method is applied to a case study located in the region of Santarem Portugal, allowing the identification of the sub-basins where the road network has the highest risks and illustrating the potential of Bayesian inference techniques for updating the model when new information becomes available.

show abstract

Section: Factors Affecting the Flood Risk Of Road Infrastructuresmentioning

confidence: 99%

Flood risk assessment for road infrastructures using Bayesian networks: case study of Santarem - Portugal

Arango

Santamaría

Nogal

et al. 2022

APP

View full text Add to dashboard Cite

show abstract

“…Ten factors are selected to evaluate urban flood inundation risk and determine inundation-prone areas in the study area according to previous studies [1,2,11,14,[32][33][34]41,44], the actual situation of the study area, the availability of data, as well as experts' knowledge and experience. The hazard factor is annual rainfall; the vulnerability factors are elevation, slope, soil water retention (SWR), river density, distance to river; the capacity factors are pipe density, road density, population density, and per unit GDP (Figure 2).…”

Section: Selection Of Criteria and Risk Factorsmentioning

confidence: 99%

“…Some AHP extension methods are also used to assess flood risk [8][9][10]. Qualitative evaluation methods also include: constant sum scale [11], entropy [12], additive weighting [13], or multi-criteria analysis approach [14,15]. There are also other methods, such as quantitative evaluation methods, many hydrological and hydrodynamic models [16][17][18][19], and a series of flood risk simulation models, such as random forest [20], gradient boosting decision tree [21], genetic algorithm [2], simulated annealing [22], rapid urban flood inundation and damage assessment model [1], meteorological research and prediction model [23], alternate decision tree [24], etc.…”

Section: Introductionmentioning

confidence: 99%

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

Huang

Wang

et al. 2021

Land

View full text Add to dashboard Cite

Drivers of urban flood disaster risk may be related to many factors from nature and society. However, it is unclear how these factors affect each other and how they ultimately affect the risk. From the perspective of risk uncertainty, flood inundation risk is considered to be the probability of inundation consequences under the influence of various factors. In this paper, urban flood inundation risk assessment model is established based on Bayesian network, and then key disaster-causing factors chains are explored through influence strength analysis. Jingdezhen City is selected as study area, where the flood inundation probability is calculated, and the paths of these influential factors are found. The results show that the probability of inundation in most areas is low. Risk greater than 0.8 account for about 9%, and most of these areas are located in the middle and southern section of the city. The influencing factors interact with each other in the form of factor chain and, finally, affect the flood inundation. Rainfall directly affects inundation, while river is the key factor on inundation which is influenced by elevation and slope. In addition, in the chain of socio-economic factors, the population will determine the pipe density through affecting gross domestic product (GDP), and lead to the inundation. The approach proposed in this study can be used to find key disaster-causing factors chains, which not only quantitatively reveal the formation of risks but also provide reference for early warning.

show abstract

“… 2 , 36 ), Bayesian belief network (e.g., Refs. 37 , 38 , or by leveraging machine learning models (e.g. Refs.…”

Section: Introductionmentioning

confidence: 99%

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

Tanim

Goharian

Moradkhani

2022

Sci Rep

View full text Add to dashboard Cite

Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals’ biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.

show abstract

A Bayesian Network-Based Integrated for Flood Risk Assessment (InFRA)

Cited by 25 publications

References 18 publications

Flood risk assessment for road infrastructures using Bayesian networks: case study of Santarem - Portugal

Flood risk assessment for road infrastructures using Bayesian networks: case study of Santarem - Portugal

Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network

Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches

Contact Info

Product

Resources

About