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一个"是”或"否”的问题。例如，通过回答以下问题：特定污染物（如PM10）是否超过了高阈值？对于这些情况，一类称为地质统计二进制模型的数学模型可以帮助我们回答许多关于我们观察到的环境指数的问题，它们也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，因为我们通常有一个不平衡的数量“是”和“否”，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。我们的目标是开发一个新的框架，结合创新的统计模型和有效的贝叶斯分析方法的基础上极值理论。该框架旨在准确估计和预测特定地区随着时间的推移发生罕见二元极端事件的概率。