RAPID: An Interactive "Human Sensor Web" for Improved Model Predictions of the Dispersion of the Deepwater Horizon Gulf Oil Spill

RAPID：用于改进深水地平线海湾漏油扩散模型预测的交互式“人体传感器网络”

• 批准号: 1061621
• 负责人: Milton Halem
• 金额: $ 13.61万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1061621&HistoricalAwards=false
• 关键词: RAPID; Interactive; Human; Sensor; Web

Proposal #: 10-61621PI(s): Halem, Milton; Brown, Sheldon; Conte, Thomas M; Yesha, YelenaInstitution: University of Maryland Baltimore CountyTitle: RAPID: Collaborative Research: An Interactive "Human Sensor Situation Network" for Improved Model Predictions of the Dispersion of the Deepwater Horizon Gulf Oil SpillProject Proposed:This project, proposing research activities related to Gnome model predictions of the Gulf oil spill (within the CHMPR I/UCRC, to be located at UMBC, UCSD, and GaTech), aims to develop an instrument that acquires servers to collect, extract, locate, and process Gulf oil spill data from the existing social media sources such as Flickr, You Tube, Twitter and integrates them into a cloud. The data is collected from mobile devices and satellite sensors. As a result, the project provides instantaneous spatial distributions and temporal frequencies of oil slicks, tar balls, distressed and dead animals, along the complete coastline of Gulf Border States. The instrument is used to perform a 2-D VAR data assimilation using the Gnome oil spill forecast model as a first guess and the social media data as boundary conditions. The project will present the forecast oil slick dispersion products on the very large LCD tiled wall at UCSD for broadcast to thousands of viewers. System delivery is expected within 15 days. Improvement upon the NOAA operational Gnome model predictions of the Gulf is also expected. Moreover, the "human sensor web" data is likely to lead to more accurate operational oil dispersion forecasts for dissemination to decision makers through the 2-D VAR data assimilation;Broader Impacts: The work prototypes what in the future should become part of the responses to future event situations, either natural or anthropogenic. The "human sensor web" data is likely to lead to more accurate operational oil dispersion forecasts for dissemination to decision makers through the 2-D VAR data assimilation; hence the social aspects are strongly evident. This project involves students, especially minorities (at UMBC). Similar outreach activities are planned at Georgia Tech. Furthermore, UCSD will organize an internship program at a middle and high school.

提案编号：10- 61621 PI（s）：Halem，米尔顿; Brown，谢尔顿; Conte，托马斯M; Yesha，Yelena机构：马里兰州巴尔的摩县大学标题： 快速：合作研究：一个交互式的“人类传感器情况网络”，用于改进深水地平线海湾溢油扩散的模型预测项目建议：该项目提出了与海湾溢油的侏儒模型预测有关的研究活动（在CHMPR I/UCRC内，将位于UMBC，UCSD和GaTech），旨在开发一种仪器，该仪器获得服务器，以收集，提取，定位和处理来自现有社交媒体来源（如Flickr，You Tube，Twitter）的海湾溢油数据，并将其集成到云中。这些数据是从移动的设备和卫星传感器收集的。因此，该项目提供了沿着海湾边界国家整个海岸线的浮油、焦油球、遇险和死亡动物的瞬时空间分布和时间频率。该仪器用于执行2-D VAR数据同化，使用Gnome石油泄漏预测模型作为第一猜测和社交媒体数据作为边界条件。该项目将在加州大学圣地亚哥分校的超大液晶瓷砖墙上展示预测的浮油分散产品，以便向数千名观众广播。系统预计在15天内交付。预计还将改进NOAA运行侏儒模型对海湾的预测。此外，“人类传感器网络”的数据可能会导致更准确的业务石油分散预测传播给决策者通过2-D VAR数据同化;更广泛的影响：工作原型什么在未来应该成为未来事件的情况下，无论是自然的或人为的反应的一部分。“人类传感器网络”的数据可能会导致更准确的业务石油扩散预测传播给决策者通过2-D VAR数据同化，因此社会方面是非常明显的。该项目涉及学生，特别是少数民族（在UMBC）。 类似的推广活动计划在格鲁吉亚技术。此外，UCSD将在初中和高中组织实习计划。