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

Abstract

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.

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

“…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.…”

“…The following step consists in discretizing all the considered sub-factors due to each node of the BN model being assigned to a finite set of state values based on discrete probabilities. According to current studies [1,15], and expert knowledge, the sub-factors impacting flood risk were categorized into three groups, as shown in Table 1.…”

“…Therefore, they are considered high uncertainty and low credibility models in risk assessment. On the other hand, quantitative evaluation methods require a large amount of data with high precision, which limits their application in some cases due to the reduced availability of data [15]. Additionally, none of the aforementioned methods define the mutual relationships between the factors that influence risk, which is often interdependent in a complex and uncertain way [1].…”

Citizens’ Preparedness to Deal with Emergencies as an Important Component of Civil Protection

“…The risk is the probability that some results will have a negative impact on people, systems, or assets, which is usually displayed as a function of the combined effects of hazards, assets, or people at risk and the vulnerability of these exposed elements [ 8 , 9 ]. The concept of risk is closely related to the concept of danger, as the risk consists of danger and vulnerability, i.e., it depends on the intensity of the danger and the level of vulnerability.…”

Towards better flood risk management using a Bayesian network approach

“…With probabilistic graphical models (PGM) such as Bayesian networks (BNs), models can be constructed from data, expert knowledge, or both, which is ideal for a data scarce environment [12][13][14]. Balbi et al [15] use commonly available information to supplement quantitative and semi-quantitative data.…”