ITR: Statistical Pattern Recognition in Environmental Observation and Forecasting Systems

ITR：环境观测和预报系统中的统计模式识别

• 批准号: 0082736
• 负责人: Todd Leen
• 金额: $ 49.91万
• 依托单位: Oregon Health & Science University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2003-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0082736&HistoricalAwards=false
• 关键词: ITR; Statistical; Pattern; Recognition; Environmental

Project SummaryEnvironmental observation and forecasting systems (EOFS) are emerging new technologies with unparalleledpotential to impact sustainable development. EOFS are expected to foster and support new paradigms forgeneration, transfer and social application of knowledge in domains that range from the global earth to itsregional and local sub-systems.At the core of EOFS is the timely and customized acquisition, generation, processing and delivery of reliable,relevant information to many and very diverse audiences. Multiple challenges need to be met to implementthis concept.A critical challenge is the development of automated procedures to verify the quality of the huge amountsof observational and simulation data that are generated by EOFS both in real-time and off-line. Process-based strategies for quality control of scientific data, while effective for moderate-size archival data are toolabor-intensive to map well into EOFS-scale data sets. Strategies based on pattern recognition and machinelearning hold significant promise as an alternative or complement.Under the proposed project, we will develop approaches based in statistical pattern recognition and signalprocessing, on-line adaptive systems, datamining, and advanced search to address critical quality controlissues including: 1) Detecting sensor corruption in non-stationary, spatial-temporal systems, 2) Estimatingtrue signals from corrupted sensor data, and 3) Detecting and characterizing regimes where model anomaliesare likely.These quality control techniques will be developed and exercised on CORIE, a pilot EOFS for the COlumbiaRIver Estuary and adjacent coastal waters (http://www.ccalmr.ogi.edu/CORIE)The project will have strong social impact, through the role of EOFS (and, specifically, CORIE) on regionaland national sustainable development issues. The project will also include cross-disciplinary educationalopportunities at multiple levels.

环境观测和预报系统（EOFS）是新兴的新技术，具有无与伦比的影响可持续发展的潜力。EOFS的核心是及时和定制的获取，生成，处理和交付可靠的，相关的信息给许多和非常多样化的受众。要实现这一概念，需要应对多种挑战。一个关键的挑战是开发自动化程序，以验证EOFS实时和离线生成的大量观测和模拟数据的质量。基于过程的科学数据质量控制战略虽然对中等规模的档案数据有效，但要很好地映射到EOFS规模的数据集，需要大量的工具。基于模式识别和机器学习的策略作为替代或补充具有重要的前景。在拟议的项目中，我们将开发基于统计模式识别和信号处理，在线自适应系统，数据挖掘和高级搜索的方法，以解决关键的质量控制问题，包括：1）检测非平稳时空系统中的传感器损坏，2）从损坏的传感器数据估计真实信号，这些质量控制技术将在CORIE上开发和实施，CORIE是一个用于哥伦比亚河口和邻近沿海沃茨的试验性EOFS（网址：//www.ccalmr.ogi.edu/CORIE）通过EOFS的作用，该项目将产生强大的社会影响（特别是CORIE）就区域和国家可持续发展问题开展的工作。该项目还将包括多层次的跨学科教育机会。