Collaborative Research: Predictive Risk Investigation SysteM (PRISM) for Multi-layer Dynamic Interconnection Analysis

合作研究：用于多层动态互连分析的预测风险调查系统（PRISM）

• 批准号: 1940291
• 负责人: Rajesh Gupta
• 金额: $ 30.3万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940291&HistoricalAwards=false
• 关键词: Collaborative; Research; Predictive; Risk; Investigation

The natural-human world is characterized by highly interconnected systems, in which a single discipline is not equipped to identify broader signs of systemic risk and mitigation targets. For example, what risks in agriculture, ecology, energy, finance and hydrology are heightened by climate variability and change? How might risks in, for example, space weather, be connected with energy, water and finance? Recent advances in computing and data science, and the data revolution in each of these domains have now provided a means to address these questions. The investigators jointly establish the PRISM Cooperative Institute for pioneering the integration of large-scale, multi-resolution, dynamic data across different domains to improve the prediction of risks (potentials for extreme outcomes and system failures). The investigators' vision is to develop a trans-domain framework that harnesses big data in the context of domain expertise to discover new critical risk indicators, holistically identify their interconnections, predict future risks and spillover potential, and to measure systemic risk broadly. The investigators will work with stakeholders to ultimately create early warnings and targets for critical risk mitigation and grow preparedness for devastating events worldwide; form wide and unique partnerships to educate the next generation of data scientists through postdoctoral researcher and student exchanges, research retreats, and workshops; and broaden participation through recruiting and training of those under-represented in STEM, including women and underrepresented minority students, and impact on stakeholder communities via methods, tools and datasets enabled by PRISM Data Library web services.The PRISM Cooperative Institute's data-intensive cross-disciplinary research directions include: (i) Critical Risk Indicators (CRIs); The investigators define CRIs as quantifiable information specifically associated with cumulative or acute risk exposure to devastating, ruinous losses resulting from a disastrous (cumulative) activity or a catastrophic event. PRISM aims to identify critical risks and existing indicators in many domains, and develop new CRIs by harnessing the data revolution; (ii) Dynamic Risk Interconnections; The investigators will dynamically model and forecast CRIs and PRISM aims to robustly identify a sparse, interpretable lead-lag risk dependence structure of critical societal risks, using state-of-the-art methods to accommodate CRI complexities such as nonstationary, spatiotemporal, and multi-resolution attributes; (iii) Systemic Risk Indicators (SRIs); PRISM will model trans-domain systemic risk, by forecasting critical risk spillovers and via the creation of SRIs for facilitating stakeholder intervention analysis; (iv) Validation & Stakeholder Engagement; The investigators will deploy the PRISM analytical framework on integrative case studies with distinct risk exposure (acute versus cumulative) and catastrophe characteristics (immediate versus sustained), and will solicit regular input from key stakeholders regarding critical risks and their decision variables, to better inform their operational understanding of policy versus practice.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by HDR and the Division of Mathematical Sciences within the NSF Directorate of Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自然与人类世界的特点是系统高度互联，单一学科无法识别更广泛的系统性风险迹象和缓解目标。例如，气候变率和变化加剧了农业、生态、能源、金融和水文领域的哪些风险？例如，太空天气中的风险如何与能源、水和金融联系起来？计算和数据科学的最新进展以及每个领域的数据革命现在都提供了解决这些问题的方法。研究人员共同建立了 PRISM 合作研究所，开创性地整合不同领域的大规模、多分辨率、动态数据，以改进风险（极端结果和系统故障的可能性）的预测。研究人员的愿景是开发一个跨领域框架，在领域专业知识的背景下利用大数据来发现新的关键风险指标，全面识别它们的相互关系，预测未来风险和溢出潜力，并广泛衡量系统性风险。调查人员将与利益相关者合作，最终制定早期预警和缓解关键风险的目标，并加强对全球毁灭性事件的准备；形成广泛而独特的合作伙伴关系，通过博士后研究员和学生交流、研究务虚会和研讨会来教育下一代数据科学家；通过招募和培训 STEM 中代表性不足的人（包括女性和代表性不足的少数族裔学生）来扩大参与，并通过 PRISM 数据库网络服务支持的方法、工具和数据集对利益相关者社区产生影响。 PRISM 合作研究所的数据密集型跨领域研究学科研究方向包括：（i）关键风险指标（CRI）；研究人员将 CRI 定义为可量化的信息，特别与灾难性（累积）活动或灾难性事件造成的破坏性、破坏性损失的累积或急性风险暴露相关。 PRISM旨在识别许多领域的关键风险和现有指标，并通过利用数据革命开发新的CRI； (ii) 动态风险关联；研究人员将动态建模和预测 CRI，PRISM 旨在稳健地识别关键社会风险的稀疏、可解释的超前滞后风险依赖结构，使用最先进的方法来适应 CRI 复杂性，例如非平稳、时空和多变量。 -分辨率属性； (iii) 系统性风险指标（SRI）； PRISM 将通过预测关键风险溢出并通过创建 SRI 来促进利益相关者干预分析，对跨域系统性风险进行建模； (iv) 验证和利益相关者参与；研究人员将在具有不同风险暴露（急性与累积）和灾难特征（立即与持续）的综合案例研究上部署 PRISM 分析框架，并将定期征求关键利益相关者关于关键风险及其决策变量的意见，以便更好地为他们提供信息。该项目是美国国家科学基金会利用数据革命 (HDR) 大创意活动的一部分，并得到 HDR 和 NSF 理事会数学科学部的共同支持数学和物理科学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Energon: A Data Acquisition System for Portable Building Analytics

Energon：用于便携式建筑分析的数据采集系统

• DOI: 10.1145/3447555.3464850
• 发表时间: 2021-06-22
• 期刊: Proceedings of the Twelfth ACM International Conference on Future Energy Systems
• 作者: Fang He;Yang Deng;Yanhui Xu;Cheng Xu;Dezhi Hong;Dan Wang
• 通讯作者: Dan Wang

Selective Sampling for Sensor Type Classification in Buildings

建筑物中传感器类型分类的选择性采样

• DOI: 10.1109/ipsn48710.2020.00028
• 发表时间: 2020-04
• 期刊: 2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN
• 作者: Ma, Jing;Hong, Dezhi;Wang, Hongning
• 通讯作者: Wang, Hongning

Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects

电网关键风险指标 (CRI)：相互关联影响的调查和讨论

• DOI: 10.1007/s10669-021-09822-2
• 发表时间: 2021-07
• 期刊: Environment Systems and Decisions
• 作者: Che;Cousin, Rémi;Daryanto, Stefani;Deng, Grace;Feng, Mei;Gupta, Rajesh K.;Hong, Dezhi;McGranaghan, Ryan M.;Owolabi, Olukunle O.;Qu, Tianyi;et al
• 通讯作者: et al