We present the methods employed by team `Uniofbathtopia' as part of the Data Challenge organised for the 13th International Conference on Extreme Value Analysis (EVA2023), including our winning entry for the third sub-challenge. Our approaches unite ideas from extreme value theory, which provides a statistical framework for the estimation of probabilities/return levels associated with rare events, with techniques from unsupervised statistical learning, such as clustering and support identification. The methods are demonstrated on the data provided for the Data Challenge -- environmental data sampled from the fantasy country of `Utopia' -- but the underlying assumptions and frameworks should apply in more general settings and applications.

翻译：本文介绍了“巴斯托邦队”在第十三届国际极值分析会议（EVA2023）数据挑战赛中所采用的方法，包括我们在第三子挑战中的优胜方案。我们的方法融合了极值理论（为稀有事件概率/重现水平估计提供统计框架）与无监督统计学习技术（如聚类与支撑识别）。这些方法在数据挑战赛提供的环境数据（采样自虚构国家“乌托邦”）上进行了验证，但其基础假设与框架应适用于更广泛的环境与应用场景。