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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. The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge

   Samadi, S; Pally, R. (April 2021, European Geosciences Union)

   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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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. Bayesian modeling of flood control networks for failure cascade characterization and vulnerability assessment

   https://doi.org/10.1111/mice.12527  

   Dong, Shangjia; Yu, Tianbo; Farahmand, Hamed; Mostafavi, Ali (December 2019, Computer-Aided Civil and Infrastructure Engineering)

   Abstract This paper presents a Bayesian network model to assess the vulnerability of the flood control infrastructure and to simulate failure cascade based on the topological structure of flood control networks along with hydrological information gathered from sensors. Two measures are proposed to characterize the flood control network vulnerability and failure cascade: (a) node failure probability (NFP), which determines the failure likelihood of each network component under each scenario of rainfall event, and (b) failure cascade susceptibility, which captures the susceptibility of a network component to failure due to failure of other links. The proposed model was tested in both single watershed and multiple watershed scenarios in Harris County, Texas using historical data from three different flooding events, including Hurricane Harvey in 2017. The proposed model was able to identify the most vulnerable flood control network segments prone to flooding in the face of extreme rainfall. The framework and results furnish a new tool and insights to help decision‐makers to prioritize infrastructure enhancement investments and actions. The proposed Bayesian network modeling framework also enables simulation of failure cascades in flood control infrastructures, and thus could be used for scenario planning as well as near‐real‐time inundation forecasting to inform emergency response planning and operation, and hence improve the flood resilience of urban areas. 

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5. Data-Driven Model Predictive Control for Real-Time Stormwater Management

   Ning, Jingyun; Bowes, Benjamin; Goodall, Jonathan; Behl, Madhur (June 2022, 2022 American Control Conference)

   Low-lying coastal cities across the world are increasingly seeing flooding due to climate change and accompanying sea-level rise. Many such cities rely on old and passive stormwater infrastructure which cannot cope up with the increasing flood risk. One potential solution for addressing coastal flooding is implementing active control strategies in stormwater systems. Active stormwater control relies on rule-based strategies, which is not able to manage the increasing flood risk. Model predictive control (MPC) for stormwater flood management is getting attention over the past decade. However, building physics-based models for MPC in stormwater management is cost and time prohibitive. In this paper, we develop a data-driven approach, which utilizes unstructured state-space models for system identification and predictive control implementation. We demonstrate our results using two real stormwater network configurations, one from the Norfolk, VA region and another model of Ann Arbor region, MI, respectively. Our results indicate that MPC outperforms rule-based strategies by up to 60% of the Norfolk model and up to 90% of the Ann Arbor model in flood management. 

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