Social and Physical Sensing Enabled Decision Support for Disaster Management and Response
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This document summarizes a project on social and physical sensing enabled decision support for disaster management. The project involves a collaboration between Kno.e.sis at Wright State University and Ohio State University. It aims to extract relevant information from citizen sensed data, develop adaptive models of hurricane storm surge coupled with citizen and remote sensed data, and provide tools to assist first responders by integrating data from multiple sources. The project will analyze multimodal data and develop methodologies to predict consequences of infrastructure damage. It is supported by the National Science Foundation.
![Three Key Questions
1. How can we extract reliable, trustworthy and relevant nuggets of
information related to civil infrastructure from language-based citizen
sensed data sets across the stages of the disaster life-cycle (preparedness,
response & recovery)? [Primary: Kno.e.sis/WSU]
2. How can we develop adaptive and dynamic models of hurricane storm
surge and flood resiliency coupled with fused information from citizen
sensed and remote sensed data? [Primary: OSU]
3. What kind of interface and tools can assist first-responders in leveraging
physical sensing (e.g. remote sensing), citizen sensing and their interactions
pertaining to infrastructure elements (e.g. chokepoints in a traffic network
due to road or bridge closures) [Shared]
3](https://image.slidesharecdn.com/hazzardsees-ppt-151201173058-lva1-app6892/85/Social-and-Physical-Sensing-Enabled-Decision-Support-for-Disaster-Management-and-Response-3-320.jpg)
Social and Physical Sensing Enabled Decision Support for Disaster Management and Response
- 1. Social and Physical Sensing Enabled Decision Support Kno.e.sis – Wright State University Component Ohio Center of Excellence in Knowledge-Enabled Computing NSF Award#: EAR 1520870 WSU PI: Amit Sheth WSU Co-PIs: T K Prasad, Valerie Shalin, WSU Graduate Students: Hussein Olimat, Pavan Kapanipathi, Sarasi Lalithsena, Siva Kumar PostDoc: Tanvi Banerjee *Kno.e.sis Center, Wright State University In collaboration with Ohio State University: ,Srinivasan Parthasarathy (contact PI), Densheng Liu (co-PI), Ethan Kubatko (co-PI) 1
- 2. Disaster Management is necessary 10 Year average -- 600 fatalities per year due to weather related disasters. $71.4 Billion damage loss in United States due to Hurricane Sandy. 2
- 3. Three Key Questions 1. How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness, response & recovery)? [Primary: Kno.e.sis/WSU] 2. How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled with fused information from citizen sensed and remote sensed data? [Primary: OSU] 3. What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a traffic network due to road or bridge closures) [Shared] 3
- 4. Extract Relevant Information Nuggets Dynamically track semantically relevant event descriptors Semantic filtering of tweet exploiting spatio- temporal-thematic context of the event descriptors4
- 5. Develop dynamic adaptive Storm Surge Models Including the information about the land, estimating storm surge needs weather related information such as: Pressure Humidity Wind Speed 5
- 6. Tools to assist first responders Semantic Mashup provides various services by integrating the data received from multiple sources. 6
- 7. Integration and Analysis of Multimodal, Multidisciplinary Data Designing methodologies to predict and help prioritize the temporally & conceptually extended consequences o damage to people, civil infrastructure and their components. Psycho- and, socio-linguistics* Semantic Web * Social Data Analysis * Network Science * Geo-spatial analysis Fluid dynamics Parallel algorithms * Research topics of Kno.e.sis’ interests in this project 7
- 9. Ohio Center of Excellence in Knowledge-Enabled Computing More at: Project Web Site NSF Award # 1520870 Lot more at: http://knoesis.org 9