TY  - JOUR
PY  - 2020//
TI  - Cascading disasters risk modeling based on linear uncertainty distributions
JO  - International journal of disaster risk reduction
A1  - Gong, Zaiwu
A1  - Wang, Yiming
A1  - Wei, Guo
A1  - Li, Lianshui
A1  - Guo, Weiwei
SP  - e101385
EP  - e101385
VL  - 43
IS  - 
N2  - Cascading disasters are a critical part of the disaster study. However, due to problems such as information incompleteness and small-sample, probability distribution functions cannot be derived based on the frequency approach. Fortunately, the uncertainty theory, designated for treating the above situations, provides a way for dealing with the problem through subjective estimations achieved by simulating uncertainty variables. This paper studies the cascading disasters risk modeling with the uncertainty theory and simulates inducing risk by using linear uncertainty distributions. To obtain the minimum early warning value and the maximum belief degree of a disaster system, a cascading disaster risk model is proposed in this paper with the characteristics of the series, parallel, and mixed risk. In addition, the early warning value represents the risk threshold of the cascading disaster system, while the belief degree represents the possibility of the cascading disaster system in a safe state under a certain early warning value level.<p />  <p>Language: en</p>
LA  - en
SN  - 2212-4209
UR  - http://dx.doi.org/10.1016/j.ijdrr.2019.101385
ID  - ref1
ER  -