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1. A New Data-Driven Approach to Measuring Hurricane Risk

   Pamukçu, D. ; Zobel, C.W. ; Arnette, A. ( May 2019 , Proceedings of the 16th ISCRAM Conference)

   Improving disaster operations requires understanding and managing risk. This paper proposes a new data-driven approach for measuring the risk associated with a natural hazard, in support of developing more effective approaches for managing disaster operations. The paper focuses, in particular, on the issue of defining the inherent severity of a hazard event, independent of its impacts on human society, and concentrates on hurricanes as a specific type of natural hazard. After proposing a preliminary severity measure in the context of a hurricane, the paper discusses the issues associated with collecting empirical data to support its implementation. The approach is then illustrated by comparing the relative risk associated with two different locations in the state of North Carolina subject to the impacts of Hurricane Florence in 2018. 

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2. Hurricane Florence (2018): Long duration single‐ and dual‐Doppler observations and wind retrievals during landfall

   https://doi.org/10.1002/gdj3.137  

   Biggerstaff, Michael I. ; Alford, A. Addison ; Carrie, Gordon D. ; Stevenson, Jeffrey A. ( November 2021 , Geoscience Data Journal)

   A ground‐based C‐band Shared Mobile Atmospheric Research and Teaching (SMART) polarimetric radar and the National Weather Service WSR‐88D radar near Wilmington, North Carolina simultaneously observed Hurricane Florence (2018) as it made landfall as a category 1 hurricane. The slow translation of Florence allowed for more than 30 hr of data collection before, during and after the tropical cyclone came ashore. Nearly 26 hr of three‐dimensional wind retrievals every 6–10 min were constructed from the radar observations, providing an unprecedented view of the evolution of rainbands, the inner core and the eyewall of Hurricane Florence. This article describes the radar data, the procedures used for automated quality control, data processing and the wind retrievals that have been constructed. The location of the data and wind retrieval archive is given. These data can be used to study the dynamics and rainfall of Hurricane Florence, to quantify the impact of winds on the natural and built environment, to validate numerical simulations of the tropical cyclone, and as an educational resource for courses in radar and tropical meteorology.

    

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3. Data to Donations: Towards In-Kind Food Donation Prediction across Two Coasts

   https://doi.org/10.1109/GHTC53159.2021.9612484  

   Sharma, Esha ; Davis, Lauren ; Ivy, Julie ; Chi, Min ( October 2021 , IEEE Global Humanitarian Technology Conference (GHTC))

   Our goal in this work is to build effective yet robust models to predict unreliable and inconsistent in-kind donations at both weekly and monthly levels for two food banks across coasts: the Food Bank of Central Eastern North Carolina in North Carolina and Los Angeles Regional Food Bank in California. We explore three factors: model, data length, and window type. For the model, we evaluate a series of classic time-series forecasting models against the state-of-the-art approaches such as Bayesian Structural Time Series modeling (BSTS) and deep learning models; for the data length, we vary training data from 2 weeks to 13 years; for the window type, we compare sliding vs. expanding. Our results show the effectiveness of different models heavily depends on the data length and the window type as well as characteristics of the food bank. Motivated by these findings, we investigate the effectiveness of employing an average of all predictions formed by considering all three factors at both monthly and weekly levels for both food banks. Our results show that this average of predictions significantly and consistently outperforms all classical models, deep learning, and BSTS for the donation prediction at both monthly and weekly levels for both food banks. 

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4. Evidence-based decision making using visual analytics for a local food bank

   https://doi.org/10.54941/ahfe1002587  

   Jiang, Steven ; Odubela, Kehinde ; Davis, Lauren ( January 2022 , AHFE International)

   Food insecurity is defined as an individual or household’s inability or limited access to safe and nutritious food that every person in the household need for an active, healthy life. In this research, we apply visual analytics, the integration of data analytics and interactive visualization, to provide evidence-based decision-making for a local food bank to better understand the people and communities in its service area and improve the reach and impact of the food bank. We have identified the indicators of the need, rates of usage, and other factors related to the general accessibility of the food bank and its programs. Interactive dashboards were developed to allow decision-makers of the food bank to combine their field knowledge with the computing power to make evidence-based informed decisions in complex hunger relief operations.

    

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5. Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band

   https://doi.org/10.3390/rs14246374  

   Salem, Abdella ; Hashemi-Beni, Leila ( December 2022 , Remote Sensing)

   Flood events have become intense and more frequent due to heavy rainfall and hurricanes caused by global warming. Accurate floodwater extent maps are essential information sources for emergency management agencies and flood relief programs to direct their resources to the most affected areas. Synthetic Aperture Radar (SAR) data are superior to optical data for floodwater mapping, especially in vegetated areas and in forests that are adjacent to urban areas and critical infrastructures. Investigating floodwater mapping with various available SAR sensors and comparing their performance allows the identification of suitable SAR sensors that can be used to map inundated areas in different land covers, such as forests and vegetated areas. In this study, we investigated the performance of polarization configurations for flood boundary delineation in vegetated and open areas derived from Sentinel1b, C-band, and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band data collected during flood events resulting from Hurricane Florence in the eastern area of North Carolina. The datasets from the sensors for the flooding event collected on the same day and same study area were processed and classified for five landcover classes using a machine learning method—the Random Forest classification algorithm. We compared the classification results of linear, dual, and full polarizations of the SAR datasets. The L-band fully polarized data classification achieved the highest accuracy for flood mapping as the decomposition of fully polarized SAR data allows land cover features to be identified based on their scattering mechanisms. 

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