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Energy Spatial Pooling for Extremes Value Inference.

用于极值推断的能量空间池。

基本信息

批准号：
2284949
负责人：
金额：
--
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2019
资助国家：
英国
起止时间：
2019 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2284949
关键词：
Energy Spatial Pooling Extremes Value

项目摘要

Safety is an overriding priority in the nuclear industry; strict rules and regulations must be maintained to avoid nuclear disasters. Such disasters often result from extreme environmental processes, such as flooding or storms. This project is in collaboration with EDF, their nuclear research and development team focus on programmes supporting the safety, performance, and life extension of existing nuclear fleet. EDF must demonstrate that their power plants are robust to rare natural hazards. This involves studying unusually high or low levels of an environmental process, then using this extreme data to extrapolate beyond what is observed to provide an insight into the probability of future extreme events. The main statistical approach for understanding the risks associated with rare events is extreme value inference, where a statistical model is fit to the extreme values of a process. Estimates of the probabilities of future extreme events are subject to large amounts of uncertainty due to a lack of available data on such rare events. Reducing this uncertainty is desirable. Since data are usually available at multiple locations, it is sensible to try to incorporate this extra information into the inference at a single site. Additionally, we will explore the joint analysis of different environmental hazards, such as wind speed, sea level and rainfall. We plan to use these approaches for borrowing information to improve current methods for estimating extreme levels of a process.In partnership with EDF Energy.

安全是核工业的首要任务;必须保持严格的规则和条例，以避免核灾难。这些灾害往往是极端环境过程造成的，如洪水或风暴。该项目与EDF合作，其核研究和开发团队专注于支持现有核舰队的安全，性能和寿命延长的计划。EDF必须证明他们的发电厂对罕见的自然灾害具有强大的抵抗力。这涉及到研究环境过程的异常高或低水平，然后使用这些极端数据来推断观察到的情况，以深入了解未来极端事件的概率。了解与罕见事件相关的风险的主要统计方法是极值推断，其中统计模型适合于过程的极值。由于缺乏关于这种罕见事件的可用数据，对未来极端事件概率的估计存在很大的不确定性。减少这种不确定性是可取的。由于数据通常在多个位置可用，因此尝试将这些额外信息合并到单个位置的推理中是明智的。此外，我们还将探索不同环境危害的联合分析，如风速、海平面和降雨量。我们计划使用这些方法来借用信息，以改善目前的方法，估计极端水平的一个过程。