Floods are among the most destructive natural hazard that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and agriculture. Internet of Things (IoTs), machine learning (ML), and Big Data are exceptionally valuable tools for collecting the catastrophic readiness and countless actionable data. The aim of this presentation is to introduce Flood Analytics Information System (FAIS) as a data gathering and analytics system. FAIS application is smartly designed to integrate crowd intelligence, ML, and natural language processing of tweets to provide warning with the aim to improve flood situational awareness and risk assessment. FAIS has been Beta tested during major hurricane events in US where successive storms made extensive damage and disruption. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 minutes). The prototype also performs flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. This presentation will discuss about the FAIS functionalities and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).
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Development of Educational Modules to Assess Flood Risk and Mitigation Strategies for Coastal Communities
Coastal Communities are exposed to multiple hazards including hurricanes, storm surges, waves, and riverine flash floods. This paper presents the outcome of a Basin-wide Flood Multi-hazard Risks module that was developed and offered as part of a collaboration between two research projects: the UPRM-DHS Coastal Resilience Center of Excellence (CRC) funded by the Department of Homeland Security and the Resilient Infrastructure and Sustainability Education Undergraduate Program (RISE-UP) funded by the National Science Foundation (NSF). The
content was designed to give students an understanding of complex project management in coastal communities. The main learning objective was for students to be able to assess and recognize the actions that can be taken to improve resiliency in coastal communities. Students learned how to manage multi-hazard floods. Through knowledge gained by participating in lectures, discussions, and the development of case studies, students were able to assess flood risk and current mitigation strategies for coastal communities in Puerto Rico. The learning experience provided an overview of the history, needs, and challenges that coastal communities face regarding flood and coastal hazards. Through the case studies, students were able to appreciate and understand the risk exposure on the natural and built infrastructure, and the importance of always taking into consideration the social impact.
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- Award ID(s):
- 1832468
- NSF-PAR ID:
- 10358371
- Date Published:
- Journal Name:
- American Society for Engineering Education Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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