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1. Examination of Diffusion Patterns of Tornado Warning Using an Agent-Based Model and Simulation

   https://doi.org/10.1175/WCAS-D-21-0089.1  

   Liang, Daan ; Cong, Zhen ; Cao, Guofeng ( April 2022 , Weather, Climate, and Society)

   Timely communication of warnings is essential to protection of lives and properties during tornado outbreaks. Both official and personal channels of communication prove to have considerable impact on the overall outcome. In this study, an agent-based model is developed to simulate warning’s reception–dissemination process in which a person is exposed to, receives, and sends information while interacting with others. The model is applied to an EF5 tornado (EF indicates enhanced Fujita scale) that struck Moore, Oklahoma, in 2013. The parameters are calibrated using publicly available data or a poststorm telephone survey or were derived from literature reviews, expert judgement, and sensitivity analysis. The result shows a reasonable agreement between modeled and observed reception rates for older and younger adults and for different channels, with errors of less than 20 percentage points. Similar agreement is also seen for the average numbers of warning sources. The subsequent simulation indicates that, in the absence of tornado sirens, the overall reception rates for younger and older adults would drop from the baseline by 17 and 6 percentage points, respectively. Concurrently, there is a large decline in the number of warning sources. When a persons’ social network is enlarged, the reception rate for older adults improves from 77% to 80%, whereas for younger adults it stays unchanged. The impact of increased connectivity is more pronounced when people are not watching television or a tornado siren is not available.

   Every year, tornadoes cause significant property damage and numerous casualties in the United States. This study aims to understand how tornado warnings reach the at-risk public through various communication channels. Using the agent-based model and simulation, we are able to reconstruct the dynamic patterns of warning’s reception–dissemination process for older and younger adults within a historical EF5 tornado. Further analysis confirms the importance of tornado sirens in not only alerting more residents about the dangerous weather condition but also prompting protective actions. In the meantime, an increase in social connectivity among residents would compensate for the lack of exposure to television and tornado siren. Future work should investigate the robustness of this model and its parameters when applied to other tornado outbreaks.

    

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2. Future crop risk estimation due to drought, extreme temperature, hail, lightning, and tornado at the census tract level in Louisiana

   https://doi.org/10.3389/fenvs.2022.919782  

   Mostafiz, Rubayet Bin ; Rohli, Robert V. ; Friedland, Carol J. ; Gall, Melanie ; Bushra, Nazla ( August 2022 , Frontiers in Environmental Science)

   Louisiana is one of the most hazard-prone states in the U.S., and many of its people are engaged directly or indirectly in agricultural activities that are impacted by an array of weather hazards. However, most hazard impact research on agriculture to date, for Louisiana and elsewhere, has focused on floods and hurricanes. This research develops a method of future crop loss risk assessment due to droughts, extreme low and high temperatures, hail, lightning, and tornadoes, using Louisiana as a case study. This approach improves future crop risk assessment by incorporating historical crop loss, historical and modeled future hazard intensity, cropland extent, population, consumer demand, cropping intensity, and technological development as predictors of future risk. The majority of crop activities occurred and will continue to occur in south-central and northeastern Louisiana along the river basins. Despite the fact that cropland is decreasing across most of the state, weather impacts to cropland are anticipated to increase substantially by 2050. Drought is by far the costliest among the six hazards, accounting for $56.1 million of $59.2 million (∼95%) in 2050-projected crop loss, followed by extreme cold ($1.4 million), extreme heat ($1.0 million), tornadoes ($0.4 million), hail ($0.2 million), and lightning ($0.05 million), respectively. These findings will assist decision-makers to minimize risk and enhance agricultural resilience to future weather hazards, thereby strengthening this economically-important industry in Louisiana and enhancing food security. 

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3. Graph Embeddings for Outage Prediction

   https://doi.org/10.1109/naps52732.2021.9654696  

   Baembitov, Rashid ; Kezunovic, Mladen ; Obradovic, Zoran ( November 2021 , 2021 North American Power Symposium (NAPS))

   This paper discusses how the risk of electricity grid outages is predicted using machine learning on historical data enhanced by graph embeddings of the distribution network. The process of graph creation using different embedding approaches is described. Several graph constructing strategies are used to create a graph, which is then transformed into the form acceptable for ML algorithm training. The impact of incorporating different graph embeddings on outage risk prediction is evaluated. The method used for graph embeddings is Node2Vec. The grid search is performed to find optimal hyperparameters of Node2Vec. The resulting accuracy metrics for a set of different hyperparameters are presented. The resulting metrics are compared against base scenario, where no graph embeddings were used. 

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4. Spatially Aware Ensemble-Based Learning to Predict Weather-Related Outages in Transmission

   T. Dokic, M. Pavlovski ( January 2019 , The Hawaii International Conference on System Sciences – HICSS, Maui, Hawaii, January 2019)

   This paper describes the implementation of a prediction model for real-time assessment of weather related outages in the electric transmission system. The network data and historical outages are correlated with a variety of weather sources in order to construct the knowledge extraction platform for accurate outage probability prediction. An extension of the logistic regression prediction model that embeds the spatial configuration of the network was used for prediction. The results show that the developed model manifests high accuracy and is able to differentiate an outage area from the rest of the network in 1 to 3 hours before the outage. The prediction model is integrated inside a weather testbed for real-time mapping of network outage probabilities based on incoming weather forecast. 

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5. The Most Frequent N-k Line Outages Occur in Motifs That Can Improve Contingency Selection

   https://doi.org/10.1109/TPWRS.2023.3249825  

   Zhou, Kai ; Dobson, Ian ; Wang, Zhaoyu ( January 2024 , IEEE Transactions on Power Systems)

   Multiple line outages that occur together show a variety of spatial patterns in the power transmission network. Some of these spatial patterns form network contingency motifs, which we define as the patterns of multiple outages that occur much more frequently than multiple outages chosen randomly from the network. We show that choosing N-k contingencies from these commonly occurring contingency motifs accounts for most of the probability of multiple initiating line outages. This result is demonstrated using historical outage data for two transmission systems. It enables N-k contingency lists that are much more efficient in accounting for the likely multiple initiating outages than exhaustive listing or random selection. The N-k contingency lists constructed from motifs can improve risk estimation in cascading outage simulations and help to confirm utility contingency selection. 

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