Probabilistic multihazard risk assessment from natural hazard is still a challenge today. Current limitations are the number of different hazards that can be included in the assessment, the capacity to output detailed spatial results and access to data for inverse fitting models. A novel quantitative multi-hazard framework is proposed which permit to model the interactions and triggering of perils of different types. Franz Josef on the West Coast of South Island, New Zealand, is used as a case study where the interactions and impacts of earthquake, rainfall, landslide, landslide dam and flooding hazards on the road network, stopbanks and housing is stochastically quantified. The results show that losses are earthquake-dominated, while rarer events show an influence of landsliding on the losses. Due to the complex interaction between hazards allowed by the modelling framework, while initial perils reach a “loss ceiling” with reducing probabilities, it also supercharges secondary perils that can cause greater and greater losses. Combining a network framework with iterative simulations is shown to provide inherent advantages by its discrete nature. In-depth exploration of the data outputs grants insight into the interconnected disaster system on a granular level. The risk assessment herein pointed out the seriousness of the multihazard threat to the Franz Josef township.

如今，从自然灾害中进行概率性多灾种风险评估仍然是一个挑战。当前的局限性是评估中可以包括的不同危害的数量，输出详细空间结果的能力以及对逆拟合模型的数据访问的能力。提出了一种新颖的定量多灾种框架，该框架允许对不同类型的危险的相互作用和触发进行建模。以新西兰南岛西海岸的弗朗兹·约瑟夫（Franz Josef）为例，对地震，降雨，滑坡，滑坡坝和洪水危害对道路网络，停车库和房屋的相互作用和影响进行了随机量化。结果表明，损失以地震为主，而罕见事件表明滑坡对损失有影响。由于建模框架允许的危害之间复杂的交互作用，虽然初始风险达到了“概率降低”的最高限额，但同时也加剧了可能造成越来越大损失的次生风险。通过将网络框架与迭代仿真结合起来，可以证明其具有固有的优势，因为它具有离散性。对数据输出的深入探索使您可以深入了解互连的灾难系统。这里的风险评估指出了对弗朗茨·约瑟夫（Franz Josef）乡镇的多灾种威胁的严重性。通过将网络框架与迭代仿真结合起来，可以证明其具有固有的优势，因为它具有离散性。对数据输出的深入探索使您可以深入了解互连的灾难系统。这里的风险评估指出了对弗朗茨·约瑟夫（Franz Josef）乡镇的多灾种威胁的严重性。通过将网络框架与迭代仿真结合起来，可以证明其具有固有的优势，因为它具有离散性。对数据输出的深入探索使您可以深入了解互连的灾难系统。这里的风险评估指出了对弗朗茨·约瑟夫（Franz Josef）乡镇的多灾种威胁的严重性。