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1. A national scale big data analytics pipeline to assess the potential impacts of flooding on critical infrastructures and communities

   https://doi.org/https://doi.org/10.1016/j.envsoft.2020.104828  

   Donratanapata, N.; Samadi, S; Vidal, J.M.; Sadeghi Tabas, S. (August 2020, Environmental modelling software)

   With the rapid development of the Internet of Things (IoT) and Big Data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big Data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management. FAIS, a national scale prototype, combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. The prototype was successfully tested in real-time during Hurricane Dorian flooding as well as for historical event (Hurricanes Florence) across the Carolinas, USA where the storm made extensive disruption to infrastructure and communities. 

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2. A national scale big data analytics pipeline to assess the potential impacts of flooding on critical infrastructures and communities

   https://doi.org/https://doi.org/10.1016/j.envsoft.2020.104828828  

   N.Donratanapat, S.Samadi (August 2020, Environmental modelling software)

   With the rapid development of the Internet of Things (IoT) and Big Data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big Data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management. FAIS, a national scale prototype, combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. The prototype was successfully tested in real-time during Hurricane Dorian flooding as well as for historical event (Hurricanes Florence) across the Carolinas, USA where the storm made extensive disruption to infrastructure and communities. 

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3. A National Scale Real Time Data Analytics Application for Assessing the Potential Impacts of Flooding on Communities

   Samadi, S. (January 2019, the American Geophysical Union)

   Successive hurricane events have brought new challenges to human life, critical infrastructure and the environment in the United States. This study introduced Flood Analytics Information System (FAIS) as a national scale data analytics application for real-time flood data collection and analysis. FAIS has been Beta tested across a mixed urban and rural basin in the Carolinas where Hurricane Florence made extensive damages and disruption. Our research aim was to develop and test an integrated solution based on real time Big data for stakeholder map-based dashboard visualizations that can be applicable to other countries and a range of weather-driven emergency situations. The prototype successfully identifies a dynamic set of at-risk locations using web-based river level and flood information. The list of prioritized locations can be updated every 15 minutes, as the environmental information and condition change. FAIS will be extended to collect data from other countries and other kinds of weather-related hazards such as snowstorms and wildfires. 

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4. Flood teleconnections from levees undermine disaster resilience

   https://doi.org/10.1038/s44304-024-00002-1  

   Ansari, Abolfazl Hojjat; Mejia, Alfonso; Cibin, Raj (December 2024, npj Natural Hazards)

   Inland levees can amplify flood risk in unprotected communities by altering floodwater levels away from their location. While these nonlocal effects of levees, which we term flood teleconnections, have been studied for specific river segments, their impact on flood risks along a river network remains underexplored. By combining data-driven, hydrodynamic, and economic models, we quantify the magnitude, spatial distribution, and economic damages associated with flood teleconnections for a large river network system with extensive levees. We find that due to levees, the 100-year flood inundation extent grows by 25% of the total levee-protected area regionally, and the flood inundation depth increases by up to 2 m at specific locations. Levees also increase the vulnerability of unprotected, marginalized communities to flooding. Our results demonstrate that flood teleconnections are spatially widespread, involve unaccounted costs, and can lead to flood inequities. These findings will be critical to climate adaptation efforts in flood-prone regions. 

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5. Challenges and Opportunities for UAV-Based Digital Elevation Model Generation for Flood-Risk Management: A Case of Princeville, North Carolina

   https://doi.org/10.3390/s18113843  

   Hashemi-Beni, Leila; Jones, Jeffery; Thompson, Gary; Johnson, Curt; Gebrehiwot, Asmamaw (November 2018, Sensors)

   Among the different types of natural disasters, floods are the most devastating, widespread, and frequent. Floods account for approximately 30% of the total loss caused by natural disasters. Accurate flood-risk mapping is critical in reducing such damages by correctly predicting the extent of a flood when coupled with rain and stage gage data, supporting emergency-response planning, developing land use plans and regulations with regard to the construction of structures and infrastructures, and providing damage assessment in both spatial and temporal measurements. The reliability and accuracy of such flood assessment maps is dependent on the quality of the digital elevation model (DEM) in flood conditions. This study investigates the quality of an Unmanned Aerial Vehicle (UAV)-based DEM for spatial flood assessment mapping and evaluating the extent of a flood event in Princeville, North Carolina during Hurricane Matthew. The challenges and problems of on-demand DEM production during a flooding event were discussed. An accuracy analysis was performed by comparing the water surface extracted from the UAV-derived DEM with the water surface/stage obtained using the nearby US Geologic Survey (USGS) stream gauge station and LiDAR data. 

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