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1. A Resilience Assessment Approach for Power System from Perspectives of System and Component Levels

   Liu, Xiaonan; Hou, Kai; Jia, Hongjie; Zhao, Junbo; Mili, Lamine: Mu; Rim, Jusong; Lei, Yunkai (January 2020, International journal of electrical power energy systems)

   This paper proposes a novel resilience assessment approach for power system. Two resilience indices are developed from the perspectives of the system and individual component levels, respectively. The former one quantifies the resilience of a power system in a system-wide manner, while the latter is intended to assess the individual component through the pre-disruption and post-disruption indices. Specifically, the pre-disruption index is used to determine the weak points of the system before the occurrence of disruptions, while the post-disruption index is for designing the optimal restoration strategies. We advocate the use of impact-increment-based state enumeration method to calculate the presented indices in an efficient way without loss of accuracy. Numerical results carried out on the IEEE RTS-79 test system and the IEEE 118-bus system validate the effectiveness of the proposed approach and indices. 

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2. Quantifying the economic impact of disasters on businesses using human mobility data: a Bayesian causal inference approach

   https://doi.org/10.1140/epjds/s13688-020-00255-6  

   Yabe, Takahiro; Zhang, Yunchang; Ukkusuri, Satish V. (December 2020, EPJ Data Science)

   null (Ed.)

   Abstract In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses ( what if the disaster did not occur? ), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes. 

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3. Revamping Sustainability Efforts Post-Disaster by Adopting Circular Economy Resilience Practices

   https://doi.org/10.3390/su152215870  

   Pradhananga, Piyush; ElZomor, Mohamed (November 2023, Sustainability)

   Post-disaster reconnaissance is vital for assessing the impact of a natural disaster on the built environment and informing improvements in design, construction, risk mitigation, and our understanding of extreme events. The data obtained from reconnaissance can also be utilized to improve disaster recovery planning by maximizing resource efficiency, minimizing waste, and promoting resilience in future disasters. This paper aims to investigate existing reconnaissance reports and datasets to identify the factors that impact the reusability of buildings post-disaster and to recommend strategies that align with circular economy goals. The study adopted a three-step research methodology to attain the proposed goals: (1) thematic analysis was used to evaluate types of damages reported in the reconnaissance reports; (2) a supervised machine-learning algorithm was employed to analyze reconnaissance datasets; and (3) a concept map was developed based on interviews of 109 stakeholders in disaster-prone communities to recommend strategies to adopt circular economy practices post-disaster. The study results highlight the recurring risks of damage to different parts of the building and how circular economy resilience practices like deconstruction can minimize waste and maximize resource efficiency during post-disaster recovery. The findings of the study promote a more regenerative economy to build resilience to the challenges of future extreme weather events. 

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4. Towards a Geospatial Household Natural Hazard Resilience Model in Rwanda

   https://doi.org/10.1109/GHTC56179.2023.10354787  

   Tomaszewski, Brian; Scott, Timothy; Schneider, Jennifer; Walker, Rebekah; Rwanyiziri, Gaspard; Christian_Kwizera, Jean Francois; Vodacek, Anthony (October 2023, IEEE)

   Perkins, Edward (Ed.)

   Household resilience to natural hazards is a critical issue facing society with the advent of climate change. In this work, we developed one of the first household natural hazard resilience geospatial models for Rwanda designed to understand household resilience at detailed spatial resolutions. We evaluated indicators within the model through empirical fieldwork using an easy-to-deploy survey on Android tablets. To the best of our knowledge, the work presented here is innovative as it is some of the first to use geospatial technology-based surveys to conduct household-level natural disaster resilience surveys in Rwanda. Select results presented in this paper indicated that household vulnerabilities and subsequent resilience generally matched existing district-level risk mapping of Rwanda. However, our work went beyond existing risk mapping to understand individual household perceptions of resilience. Respondents generally reported a mix of positive and negative drivers of household resilience. Security vis-à-vis natural disasters and economic situation was perceived as very insecure, healthcare and education were very secure, and utilities, food and water, and housing were generally perceived as insecure but not as insecure as economic situation and security to future disasters. There is much more that can be understood in terms of household resilience as it relates to many factors of household resiliency in our model including physical vulnerabilities, financial capacity, information access, technological capacity, and most importantly, resilience perceptions. 

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5. A Machine learning approach for Post-Disaster data curation

   https://doi.org/10.1016/j.aei.2024.102427  

   Ho Ro, Sun; Li, Yitong; Gong, Jie (February 2024, Advanced Engineering Informatics)

   Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development. 

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