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1. 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 functionalitiesmore »and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).« less
2. Framing the Problem of Flood Risk and Flood Management in Metropolitan Los Angeles

   https://doi.org/10.1175/WCAS-D-22-0013.1  

   Ulibarri, Nicola ; Valencia-Uribe, Claudia ; Sanders, Brett F. ; Schubert, Jochen ; Matthew, Richard ; Forman, Fonna ; Allaire, Maura ; Brady, David ( January 2023 , Weather, Climate, and Society)

   This paper develops the concept of flood problem framing to understand decision-makers’ priorities in flood risk management in the Los Angeles Metropolitan Region in California (LA Metro). Problem frames shape an individual’s preferences for particular management strategies and their future behaviors. While flooding is a complex, multifaceted problem, with multiple causes and multiple impacts, a decision-maker is most likely to manage only those dimensions of flooding about which they are aware or concerned. To evaluate flood decision-makers’ primary concerns related to flood exposure, vulnerability, and management in the LA Metro, we draw on focus groups with flood control districts, city planners, nonprofit organizations, and other flood-related decision-makers. We identify numerous concerns, including concerns about specific types of floods (e.g., fluvial vs pluvial) and impacts to diverse infrastructure and communities. Our analyses demonstrate that flood concerns aggregate into three problem frames: one concerned with large fluvial floods exacerbated by climate change and their housing, economic, and infrastructure impacts; one concerned with pluvial nuisance flooding, pollution, and historic underinvestment in communities; and one concerned with coastal and fluvial flooding’s ecosystem impacts. While each individual typically articulated concerns that overlapped with only one problem frame, each problem frame was discussed by numerousmore »organization types, suggesting low barriers to cross-organizational coordination in flood planning and response. This paper also advances our understanding of flood risk perception in a region that does not face frequent large floods.

   This paper investigates the primary concerns that planners, flood managers, and other decision-makers have about flooding in Southern California. This is important because the way that decision-makers understand flooding shapes the way that they will plan for and respond to flood events. We find that some decision-makers are primarily concerned with large floods affecting large swaths of infrastructure and housing; others are concerned with frequent, small floods that mobilize pollution in low-income areas; and others are concerned with protecting coastal ecosystems during sea level rise. Our results also highlight key priorities for research and practice, including the need for flexible and accessible flood data and education about how to evacuate.

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3. 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.
4. 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.
5. Local floods induce large-scale abrupt failures of road networks

   https://doi.org/10.1038/s41467-019-10063-w  

   Wang, Weiping ; Yang, Saini ; Stanley, H. Eugene ; Gao, Jianxi ( May 2019 , Nature Communications)

   The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods.