GEOBEx: Geostatistical Binary Models For Extremes

GEOBEx：极值地统计二元模型

• 批准号: EP/Y031229/1
• 负责人: Daniela Andrea Castro-Camilo
• 金额: $ 4.93万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY031229%2F1
• 关键词: GEOBEx; Geostatistical; Binary; Models; Extremes

Assessing risks in environmental problems, such as contamination, heatwaves, temperature, and floods, is of utmost importance for biodiversity and human health. In many cases, this need for risk assessment can be easily translated into a ``yes" or ``no" problem. For example, by answering the question: does a specific pollutant, such as PM10, exceed a high threshold?For these cases, a class of mathematical models called geostatistical binary models can help us answer many questions regarding the environmental index we are observing, and they can also help us predict the occurrence of high index values in places where we do not observe it.However, fitting these models can be difficult since we usually have an imbalanced quantity of ``yes" and ``no", which limits the amount of information.This project studies the levels of a specific pollutant (PM10) in Mexico City. The goal is to develop a new framework that combines innovative statistical models and efficient Bayesian analysis methods based on extreme-value theory. This framework aims to accurately estimate and predict the probability of rare, binary extreme events in specific regions over time.

评估污染、热浪、温度和洪水等环境问题的风险对于生物多样性和人类健康至关重要。在许多情况下，这种风险评估的需要可以很容易地转化为“是”或“否”的问题。例如，通过回答这样的问题：某种特定的污染物，例如PM10，是否超过了高阈值？对于这些情况，一类称为地统计二元模型的数学模型可以帮助我们回答有关我们正在观察的环境指数的许多问题，并且它们还可以帮助我们预测在我们没有观察到的地方出现高指数值。然而，拟合这些模型可能很困难，因为我们通常有“是”和“否”的数量不平衡，这限制了信息量。该项目研究墨西哥城特定污染物 (PM10) 的水平。目标是开发一个新的框架，结合创新的统计模型和基于极值理论的高效贝叶斯分析方法。该框架旨在准确估计和预测特定区域随时间推移发生罕见二元极端事件的概率。