BIGDATA: F: DKA: Collaborative Research: High-Dimensional Statistical Machine Learning for Spatio-Temporal Climate Data

BIGDATA：F：DKA：协作研究：时空气候数据的高维统计机器学习

• 批准号: 1447566
• 负责人: Arindam Banerjee
• 金额: $ 35.7万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1447566&HistoricalAwards=false
• 关键词: BIGDATA; DKA; Collaborative; Research; Dimensional

While statistical machine learning has seen major advances over the past two decades, rigorous approaches for high-dimensional spatio-temporal scientific data analysis have not received as much attention. On the other hand, several core scientific areas, including climate science, ecology, environmental sciences, and neuroscience, are generating increasing amounts of high-resolution spatio-temporal data. It is vital to develop rigorous machine learning approaches for such complex high-dimensional spatio-temporal data in order for key scientific breakthroughs in these areas in the next few decades. The project contributes to these endeavors by focusing on two key technical and scientific areas: spatio-temporal big data analysis and climate science. The project systematically develops the statistical machine learning foundations for the analysis of large scale complex high-dimensional spatio-temporal data, and applies such advances to problems arising in climate science, where the total amount of data is set to cross an Exabyte (1 Exabyte = 1000 Petabytes) soon. The technical work in the project has three broad and interacting components: structured probabilistic graphical models for spatio-temporal data analysis, generalized graphical models for multivariate heavy tailed distributions, and physics-guided models with a richer class of structural constraints and capturing multi-scale phenomena. The project applies these technical advances to climate science, by generating climate projections at high-resolutions. Currently, the lack of requisite spatial resolution of current climate models makes automatic assessments of impacts, adaptation and vulnerability (IAV) difficult for a variety of sectors, including urban planning, freshwater resources, food security, energy, transportation systems, human health, and coastal systems.

尽管统计机器学习在过去的二十年中取得了重大进展，但高维时空科学数据分析的严格方法并没有得到太多关注。 另一方面，包括气候科学，生态学，环境科学和神经科学在内的几个核心科学领域正在产生越来越多的高分辨率时空数据。 为这种复杂的高维时空数据开发严格的机器学习方法至关重要，以便在未来几十年中在这些领域进行关键的科学突破。 该项目通过关注两个关键的技术和科学领域来促进这些努力：时空大数据分析和气候科学。 该项目系统地开发了统计机器学习基础，以分析大规模复杂的高维时空数据，并将此类进步应用于气候科学中产生的问题，其中将总数据设置为跨越exabyte（1个exabyte = 1000 petabytes）。该项目的技术工作具有三个广泛的相互作用组件：用于时空数据分析的结构化概率图形模型，用于多元重型尾部分布的广义图形模型，以及具有更丰富的结构约束和捕获多层现象的物理学指导的模型。 该项目通过在高分辨率上产生气候预测，将这些技术进步应用于气候科学。 目前，缺乏当前气候模型的必要空间分辨率可以自动评估影响，适应性和脆弱性（IAV）的自动评估，包括城市规划，淡水资源，粮食安全，能源，运输系统，人类健康和沿海系统，包括城市规划，淡水资源，能源，运输系统和沿海系统。