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Title: Modelling multi-hazard risk assessment : a case study in the Yangtze River Delta, China
Author: Liu, Baoyin
ISNI:       0000 0004 5368 5795
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2015
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Multi-hazard risk assessment (MHRA) has become a major concern in the risk study area, but existing approaches do not adequately meet the needs of risk mitigation planning. The main research gap in the existing approaches was identified that they cannot consider all hazard interactions when calculating possible losses. Hence, an improved MHRA model, MmhRisk-HI (Model for multi-hazard Risk assessment with a consideration of Hazard Interaction), was developed. This model calculates the possible loss caused by multiple hazards, with an explicit consideration of interaction between different hazards. A more complete perspective, the regional disaster system perspective, was selected as the basic theory, and two categories of multi-hazard risk expressions were combined in the model construction. Hazard identification, hazard analysis, hazard interaction analysis, exposure analysis and vulnerability analysis are the five basic modules of the developed model. The concept of hazard-forming environment was introduced into the MHRA research as the basis for hazard identification, hazard analysis, and hazard interaction analysis. The methods used for exposure analysis depend on the scale of the region to be addressed and the assessment units. A Bayesian Network was adopted to calculate the loss ratio in the vulnerability analysis. This developed model was applied into the Yangtze River Delta (YRD) and validated by comparison with an observed multi-hazard sequence. The validation results (simulation results are consistent with observed results in 76.36% of the counties, and the deviation of an estimated aggregate loss value from its actual value is less than 2.79%) show that this model can more effectively represent the real world, and that the outputs, possible loss caused by multiple hazards, obtained with the model are reliable. The outputs can additionally help to identify which area is at greatest risk (of loss), and allow a determination of the reasons that contribute to the greatest losses. Hence, it is a useful tool which can provide further information for planners and decision-makers concerned with risk mitigation.
Supervisor: Siu, Yim Ling ; Mitchell, Gordon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available