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Probabilistic framework for quantifying human flight failure rate to landslides
Engineering Geology ( IF 6.9 ) Pub Date : 2024-09-06 , DOI: 10.1016/j.enggeo.2024.107723 Shuairong Wang , Shuai Zhang , Yanbo Chen , Dalei Peng , Te Xiao , Yiling Zhou , Cong Dai , Limin Zhang
Engineering Geology ( IF 6.9 ) Pub Date : 2024-09-06 , DOI: 10.1016/j.enggeo.2024.107723 Shuairong Wang , Shuai Zhang , Yanbo Chen , Dalei Peng , Te Xiao , Yiling Zhou , Cong Dai , Limin Zhang
Landslides pose a severe risk to humans, but accurately quantifying human risk remains challenging due to the less-studied fleeing process of humans during landslides. This study introduces a flight failure rate to represent the capacity of humans to escape from a landslide. A novel probabilistic framework for flight failure rate assessment is proposed by integrating uncertainties in both landslide runout and human flight. This framework distinguishes the individual flight failure rates at different locations and the total flight failure rate of the population in a landslide-threatened area. To aid in applying this framework in real-world communities, a network-based human flight model, embedded with the Ant Colony Optimization algorithm, is developed to simulate the heterogeneous human flight behaviors subjected to landslides. A catastrophic landslide in a community of Shenzhen, China, which caused 77 deaths, 17 injuries, and 900 homeless, serves as a case study to perform human flight simulation and flight failure rate assessment. Results indicate that the approach provides reliable and logical evaluations of individual and total flight failure rates. Individual flight failure rate varies significantly in spatial distribution due to differences in landslide available time and running distances to escape the landslide, which differs from the total flight failure rate of the population. Advancing and narrowing the distribution of response time, reducing the delayed time, and implementing pre-planned flight paths can significantly reduce the total flight failure rate and mitigate high-risk areas. This probabilistic framework provides a promising and valuable reference for landslide risk assessment and human disaster mitigation.
更新日期：2024-09-06
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