Using Generative Models to Produce Realistic Populations of the United Kingdom Windstorms

Etron Yee Chun Tsoi
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Abstract

Windstorms significantly impact the UK, causing extensive damage to property, disrupting society, and potentially resulting in loss of life. Accurate modelling and understanding of such events are essential for effective risk assessment and mitigation. However, the rarity of extreme windstorms results in limited observational data, which poses significant challenges for comprehensive analysis and insurance modelling. This dissertation explores the application of generative models to produce realistic synthetic wind field data, aiming to enhance the robustness of current CAT models used in the insurance industry. The study utilises hourly reanalysis data from the ERA5 dataset, which covers the period from 1940 to 2022. Three models, including standard GANs, WGAN-GP, and U-net diffusion models, were employed to generate high-quality wind maps of the UK. These models are then evaluated using multiple metrics, including SSIM, KL divergence, and EMD, with some assessments performed in a reduced dimensionality space using PCA. The results reveal that while all models are effective in capturing the general spatial characteristics, each model exhibits distinct strengths and weaknesses. The standard GAN introduced more noise compared to the other models. The WGAN-GP model demonstrated superior performance, particularly in replicating statistical distributions. The U-net diffusion model produced the most visually coherent outputs but struggled slightly in replicating peak intensities and their statistical variability. This research underscores the potential of generative models in supplementing limited reanalysis datasets with synthetic data, providing valuable tools for risk assessment and catastrophe modelling. However, it is important to select appropriate evaluation metrics that assess different aspects of the generated outputs. Future work could refine these models and incorporate more ...
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使用生成模型生成英国风灾的现实种群
风灾对英国的影响很大,造成了巨大的财产损失,扰乱了社会秩序,并可能导致人员伤亡。对此类事件的准确建模和理解对于有效评估和减轻风险至关重要。然而,极端风灾的罕见性导致观测数据有限,这给全面分析和保险建模带来了巨大挑战。本论文探讨了如何应用生成模型生成逼真的合成风场数据,旨在增强保险业当前使用的 CAT 模型的稳健性。研究利用了ERA5数据集的每小时再分析数据,时间跨度为1940年至2022年。研究采用了三种模型,包括标准 GANs、WGAN-GP 和 U-net 扩散模型,以生成高质量的英国风图。然后使用 SSIM、KL 分歧和 EMD 等多个指标对这些模型进行了评估,其中一些评估是在使用 PCA 的降维空间中进行的。结果显示,虽然所有模型都能有效捕捉一般空间特征,但每个模型都表现出不同的优缺点。与其他模型相比,标准 GAN 模型引入了更多噪声。WGAN-GP 模型表现出卓越的性能,尤其是在复制统计分布方面。U-net 扩散模型产生了最直观一致的输出,但在复制峰值强度及其统计变异性方面略显吃力。这项研究强调了生成模型在用合成数据补充有限的再分析数据集方面的潜力,为风险评估和灾难建模提供了宝贵的工具。然而,重要的是要选择适当的评估指标,对生成输出的不同方面进行评估。未来的工作可以完善这些模型,并纳入更多...
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