Statistical models for multihazards : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand

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Massey University
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Natural hazards such as earthquakes, floods and landslides threaten communities in every part of the world. Exposure to such perils can be reduced by mitigation and forward planning. These procedures require the estimation of event likelihoods, a process which is well understood for single hazards. However, spatio-temporal interaction between natural hazards, through triggering or simple coincidence, is not uncommon (e.g. Alaska 1964, the Armero tragedy, the Kaikoura earthquake), and can lead to more severe consequences than the simple sum of two separate events. Hence single hazard assessments may underestimate, or incorrectly estimate, the real risk through a lack of interaction analysis. In the existing research literature, multi-hazards assessments are most commonly approached qualitatively or semi-quantitatively, evaluating hazards via an interaction matrix, without formal quantification of the risk. This thesis presents a quantitative framework, using point processes as the key tool, to evaluate the interaction of primary hazards in the occurrence of secondary (triggered) ones. The concept of the ‘hazard potential’ is developed, as a means of generalizing hazard interactions in space and time, allowing event outcomes to be simulated within a simple point process framework. Two particular examples of multiple hazard interactions are presented: rainfall and/or earthquake-induced landslides, and the survival of landslide dams. In the first case, point processes are used to model the triggering influence of multiple factors in a large real dataset collected from various sources. By discretizing space and time to match the data resolution, a daily-spatio-temporal hazard model to evaluate the relative and combined effects on landslide triggering due to earthquakes and rainfall is created. The case study on the Italian region of Emilia-Romagna suggests that the triggering effects are additive. In the second example, a Bayesian survival model is developed to forecast the time to failure of landslide dams, based on their characteristics and those of the potential reservoir. A case study on heterogeneous Italian events is presented, together with examples of potential results (forecasting) and possible generalizations of the model.
Listed in 2020 Dean's List of Exceptional Theses
Figures 3.2 and 3.3 are re-used under a Creative Commons license (CC BY 3.0 and CC BY 4.0 respectively). Figures 6.8 and 7.1 are re-used with the publishers' permission.
Natural disasters, Mathematical models, Statistical methods, Forecasting, Italy, Dean's List of Exceptional Theses