The objectives of this study were to investigate(1) gender and race differences in mental health and psychological wellbeing among older adults during the COVID-19 pandemic and(2) whether there were significant intera...The objectives of this study were to investigate(1) gender and race differences in mental health and psychological wellbeing among older adults during the COVID-19 pandemic and(2) whether there were significant interaction effects between gender and race. This study used the National Health and Aging Trends Study(NHATS) and COVID-19 public use data files. It focused on mental health and psychological well-being, including loneliness, no time to yourself, poor sleep quality, anxiety, depression, and post-traumatic stress disorder(PTSD). The sample consisted of 2465 older adults, and multivariate logistic and linear regression models were adopted for the analysis. The results show that approximately two out of five older people(43%) experienced poor sleep, followed by loneliness(35%), anxiety(30%), depression(23%), and no time to themselves(11%). The average PTSD score was 11. Mental health and psychological well-being varied by gender and race. There were significant interaction effects on anxiety and depression between females and Black older adults. Black females reported lower levels of anxiety and depressive symptoms than Black males, although the differences were not statistically significant. Black older adults were more likely to experience PTSD but less likely to feel lonely and have poorer sleep than their White counterparts. Female older adults reported poorer mental health and psychological well-being than males, except for Black females. Black females had the lowest levels of anxiety, depression, loneliness, and highest quality of sleep among all females of different ethnic and racial origins.展开更多
Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognize...Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognized strengths that can be utilized pre-, peri-, and post-disaster. One of these strengths is older adults' unique social capital that stems from long-standing connections with other members of their respective communities. Using data from in-depth, semistructured interviews with farmers in British Columbia 3–11 months after the 2021 floods, this research explored the experiences of older adult farmers' recovery. The farmers discussed how they leveraged their social capital to aid in their recovery efforts from the flood event. By using their bonding social capital, older adult farmers transformed their existing, deep-rooted connections into post-disaster assistance. This, in turn, generated the idea of the therapeutic community, helping community members cope in the aftermath. This research indicated the need to further examine how older adults in disaster settings can be viewed as assets with community knowledge and skills as opposed to solely as a vulnerable population.展开更多
This article presents the results of a study that evaluated VinclesBCN during COVID-19. This digital-based public social service aims to prevent loneliness and isolation in +65-year-old adults living in Barcelona. Thr...This article presents the results of a study that evaluated VinclesBCN during COVID-19. This digital-based public social service aims to prevent loneliness and isolation in +65-year-old adults living in Barcelona. Through service user(N = 12) and professional(N = 6) interviews and a questionnaire with service users(N = 255), we demonstrate the pivotal role of digital connectedness in transforming VinclesBCN into a lifeline during the pandemic. The analysis revealed the importance of sociability, social support, and, especially, entertainment in coping with pandemic fatigue and facilitating social connectivity and support among users. Users engaged in activities such as sharing images, songs, memes, and daily greetings to provide proximity, sociability, and care among users, whether they belonged to preexisting groups or were newly introduced to the platform. It also facilitated the identification of individuals who needed companionship, comfort, or more specialized support. The findings emphasize the significance of entertainment as a resilience-building strategy during times of uncertainty. Despite the positive impact, not all users equally used the platform. Non-use was strongly associated with being a woman, having a low educational level, having preexisting social relationships, less time of enrolment in the platform, as well as a high perception of loneliness, poor self-reported health, and low mood. The article underscores the need for further research into older adults' digital engagement during crises, its role in building resilience, and advocating for inclusive digital interventions that take into account diverse older adults' needs and experiences in crisis contexts.展开更多
The disproportionate risks and impacts of climate change and extreme weather on older adults are increasingly evident. While especially true in disaster-prone areas, human-caused climate change introduces an element o...The disproportionate risks and impacts of climate change and extreme weather on older adults are increasingly evident. While especially true in disaster-prone areas, human-caused climate change introduces an element of uncertainty even in previously identified “safe” regions such as the Midwestern United States. Using a cumulative disadvantage and vulnerability-informed framework and descriptive statistics from multiple data sources, this article provides an overview of climate impacts, vulnerabilities, and county-level characteristics, focusing on older adults living in Central Ohio. A comparative multiple-case study methodology was used to triangulate regionally representative primary and secondary data sources to examine state and county-level measures of vulnerability, emergency preparedness, and disruptions caused by extreme weather among older adults across eight counties in Central Ohio. Seventy-eight percent of older adults in the sample reported being prepared for emergencies per Federal Emergency Management Agency guidelines. Older adults in Union County reported the highest rates of preparedness, while those in Fayette County reported the lowest. County-level rates of disruption of life activities by extreme weather ranged widely. Among the most rural in the region, Fayette County emerged as uniquely disadvantaged, with the lowest median income, the most vulnerable across multiple social vulnerability dimensions, and the most reported disruptions to life activities from extreme weather. County profiles offer a snapshot of existing vulnerabilities, socioeconomic conditions, special needs, preparedness, and current disruptions among older adults in the region and can inform resource mobilization across community and policy contexts.展开更多
Shifting demographics—that is, the growing number of older adults in Canada and internationally—and intersecting climatic risks create a complex landscape of aging in place in disaster contexts. Some older adults ar...Shifting demographics—that is, the growing number of older adults in Canada and internationally—and intersecting climatic risks create a complex landscape of aging in place in disaster contexts. Some older adults are vulnerable due to their underlying health conditions and limited physical mobility. Yet they also exhibit resilience in a distinct manner, owing to their wealth of knowledge, wisdom, and ability to navigate adversities. This article elucidates potential avenues for promoting the involvement of older adults in disaster recovery efforts. Our intention is to recognize and foster older adult resilience and mitigate vulnerability by drawing upon insights from the study titled “In the Aftermath of the 2016 Alberta Wildfires: The Role of Social Work Practitioners and Human Service Professionals in Long-Term Disaster Recovery.” Employing a qualitative research approach, that study recruited 51 participants for semistructured interviews and focus groups. A thematic analysis of the collected data unveiled the pivotal roles played by social workers and human service practitioners in facilitating recovery from the 2016 wildfires in the research. Notably, three overarching themes emerged concerning the promotion of older adults' involvement in disaster recovery: fostering supportive relationships, building partnerships and enhancing collaboration, and addressing ongoing challenges. These findings offer valuable insights into how social work practitioners and human service professionals can effectively facilitate the engagement of older adults in disaster recovery initiatives. This article emphasizes the significance of actively promoting the participation of older adults in disaster mitigation efforts, thereby fostering greater resilience within communities.展开更多
Older adults are significantly impacted by natural hazards and disasters that are exacerbated by climate change. Understanding their awareness and preparedness is essential for enhancing disaster resilience. This stud...Older adults are significantly impacted by natural hazards and disasters that are exacerbated by climate change. Understanding their awareness and preparedness is essential for enhancing disaster resilience. This study investigated the attitudes, actions, and recommendations of older adults regarding natural hazards that pose risks in their geographic area—specifically floods, wildfires, and/or earthquakes in Canada. Methods for this study included survey and focus groups with older adults(n = 161 and n = 10, respectively) and other high-risk groups from across Canada, that are vulnerable to these natural hazards. The main findings from this study are that current awareness and preparedness among older adults is low, though stronger perceptions of risks are associated with risks specific to geographic locations where respondents live. Several barriers, such as hazard vulnerability misperceptions, cost-related reasons, and lack of hazard awareness have resulted in low awareness and preparedness among these populations. The two main recommendations arising from this research are:(1) improve awareness and preparedness with tailor-made emergency preparedness materials for older adults;and(2) adopt community-based approaches to disaster preparedness through existing community groups to strengthen social connections with a focus on locally specific hazards. The findings from this research can be applied to other hazards, including heatwaves and pandemics.展开更多
Despite the well-documented impacts of single natural hazards like earthquakes, less is known about the psychological adaptation to multiple natural hazards, particularly in rural areas. This study investigated the as...Despite the well-documented impacts of single natural hazards like earthquakes, less is known about the psychological adaptation to multiple natural hazards, particularly in rural areas. This study investigated the associations of multiple natural hazards with depression among Chinese adults. Data were retrieved from the China Family Panel Studies conducted during 2010–2018. With a sample of 11,633 Chinese adults, multilevel logistic regression was employed to examine the relationships between natural hazard exposure and depression in the total sample and different age groups. Overall, experiencing four or more natural hazards was associated with a higher risk of being depressed. Regarding hazard type, the number of hydrometeorological and biological hazards was associated with a higher likelihood of depression, whereas the number of geologic and other hazards was related to a lower risk of depression. Middle-aged adults from villages were more likely to be affected by natural hazard exposure than older and younger adults. The findings of this study show that cumulative exposure to natural hazards can generate lasting effects on depressive symptoms, particularly in middle adulthood. The findings also suggest that older adults from rural areas may have accumulated more resilience to mitigate the adverse well-being effects of hazard events. Policies and interventions should enhance disaster awareness and preparation for aging residents from multi-hazard communities.展开更多
During times of crisis, including pandemics, climate change, and forced migration, much of the discourse in ageing research and intervention centers on the vulnerabilities of older adults. Unfortunately, the valuable ...During times of crisis, including pandemics, climate change, and forced migration, much of the discourse in ageing research and intervention centers on the vulnerabilities of older adults. Unfortunately, the valuable contributions of older adults to post-disaster recovery and healing are often overlooked and undervalued. Our aim in this scoping review is to shed light on the critical contributions of older forced migrants to post-migration recovery. We set the scene by introducing the two significant global demographic changes of the twenty-first century: forced migration and ageing. We provide a discourse on older forced migrants, ageing in situations of forced migration, and some of the challenges faced by older forced migrants. We then present some of the substantial roles of older forced migrants in post-migration recovery, including building resilience, contributing to culture and language transfer, providing emotional support, offering mentorship and leadership, participating in community building, and fostering social integration. We close by highlighting some of the lessons that can be drawn from understanding the unique roles played by older adults in post-forced migration recovery and the key actions necessary to promote these roles.展开更多
When COVID-19 devastated older-adult organizations(long-term care homes and retirement homes), most public attention was directed toward the older-adult residents rather than their service providers. This was especial...When COVID-19 devastated older-adult organizations(long-term care homes and retirement homes), most public attention was directed toward the older-adult residents rather than their service providers. This was especially true in the case of personal support workers, some of whom are over the age of 55, putting them in two separate categories in the COVID-19 settings:(1) a vulnerable and marginalized group who are disproportionately impacted by COVID-19;and(2) essential healthcare workers. Since the current disaster-driven research, practice, and policy have primarily focused on generalized assumptions that older-adults are a vulnerable, passive, and dependent group rather than recognizing their diversity, expertise, assets, and experiences, this study aimed to identify their contributions from the perspective of older-adult personal support worker(OAPSW). This qualitative study conducted in-depth interviews, inviting 15 OAPSWs from the Greater Toronto Area, Canada. This study uncovered the OAPSWs' contribution at three levels: individual(enhancing physical health, mental health, and overall well-being), work(improving working environment and service and supporting co-workers), and family(protecting their nuclear and extended families). The outcomes inform the older-adult research, practice, policy, public discourse, and education by enhancing the appreciation of older-adults' diverse strengths and promoting their engagement and contributions in disaster settings.展开更多
Vulnerability evaluation plays a key role in risk assessment and reduction and is essential for defining strategies for climate change adaptation and mitigation.In dealing with the safeguarding of cultural heritage at...Vulnerability evaluation plays a key role in risk assessment and reduction and is essential for defining strategies for climate change adaptation and mitigation.In dealing with the safeguarding of cultural heritage at risk,we are still far from adopting and applying an agreed methodology for vulnerability assessment.With the aim to support practitioners,heritage managers,and policy and decision makers to undertake actions that address the protection of cultural heritage at risk,the methodology set up in the framework of the Interreg Central Europe STRENCH is illustrated and discussed here.Based on three major requirements(susceptibility,exposure,and resilience)and a continuous consultation with local stakeholders,the proposed methodology is applicable for evaluating the vulnerability of built heritage and cultural landscape exposed to hydrometeorological hazards,such as heavy rains,floods,and droughts.The results obtained through its validation on 15 case studies from seven Central European regions are shown to underline the strengths and limitations of the methodological approach.Iterative consultation with local stakeholders was fundamental for the definition of the criteria/subcriteria and related values for the assessment of the requirements.Application to further sites in other contexts would surely contribute to strengthening the reliability of the methodological approach.展开更多
Due to a lack of resources,rural communities often face challenges when planning catastrophic events.This project involved applying systems thinking and model-based systems engineering to develop a proof-of-concept,mu...Due to a lack of resources,rural communities often face challenges when planning catastrophic events.This project involved applying systems thinking and model-based systems engineering to develop a proof-of-concept,multi-method computer simulation and then determining whether the simulation could be used to assess the efficacy of disaster planning approaches on health outcomes in rural communities,as a function of primary healthcare.The project focus was a rural or non-urban healthcare system experiencing a natural hazard.Both system dynamics and discrete event models were incorporated to represent subsystem operations,crucial disaster responses,as well as three key response systems:public health,emergency management,and healthcare.The subsystem models included several components:policies/procedures,communications,resources,exercises/drills/training,healthcare space and staff,and the flow of affected people into and through the system.The combined simulation can serve as a first step to a more comprehensive approach to helping rural communities achieve more efficient and effective healthcare planning for disaster responses.展开更多
A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the econ...A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.展开更多
Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral...Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.展开更多
This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network r...This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.展开更多
Global and national policy frameworks emphasize the importance of people's participation and volunteers'role in disaster risk reduction.While research has extensively focused on volunteers in disaster response...Global and national policy frameworks emphasize the importance of people's participation and volunteers'role in disaster risk reduction.While research has extensively focused on volunteers in disaster response and recovery,less attention has been paid on how organizations involved in disaster risk management can support volunteers in leading and coordinating communitybased disaster risk reduction.In 2019,the New Zealand Red Cross piloted the Good and Ready initiative in Auckland,Aotearoa New Zealand,with the objective to empower local people in resilience building with a focus on volunteers and community participation.This research examined the positive and negative outcomes of Good and Ready and investigated volunteers'experiences in the disaster resilience initiative.It involved the codesign of a questionnaire-based survey using participatory methods with Good and Ready volunteers,the dissemination of the survey to gather volunteers'viewpoints,and a focus group discussion with participatory activities with Red Cross volunteers.The findings highlight that a key challenge lies in finding a balance between a program that provides flexibility to address contextual issues and fosters communities'ownership,versus a prescriptive and standardized approach that leaves little room for creativity and self-initiative.It pinpoints that supporting volunteers with technical training is critical but that soft skills training such as coordinating,communicating,or facilitating activities at the local level are needed.It concludes that the sustainability of Good and Ready requires understanding and meeting volunteers'motivations and expectations and that enhancing partnerships with local emergency management agencies would strengthen the program.展开更多
Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current stud...Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.展开更多
Leadership courses in the fire services are highly challenging,and they can seriously exhaust trainees and hamper their selfregulated learning efforts(for example,setting goals,focusing attention,seeking feedback).We ...Leadership courses in the fire services are highly challenging,and they can seriously exhaust trainees and hamper their selfregulated learning efforts(for example,setting goals,focusing attention,seeking feedback).We theorize that experiences of failure or overload can curtail trainees'available energy resources on subsequent training days,which,in turn,should affect trainees'learning efforts.Given instructors'central role in leadership courses,we hypothesize that supportive and humble instructor behaviors decrease experiences of failure and overload and,thus,increase self-regulated learning.Moreover,we argue that supportive instructor behavior may amplify the positive effects of high energy resources,while humble behavior may alleviate the negative impact of low resource levels.We tested preregistered hypotheses with 118 firefighters participating in two-week leadership courses at a German fire academy.The participants completed short web-based questionnaires before and after classes each day.Multilevel analyses confirmed that perceived daily supportive and humble instructor behavior predicted trainees'reports of daily self-regulated learning activity.Notably,this effect was independent from positive effects of trainees'reported energy resources in the morning.Supportive and humble behavior did not moderate the eff ect of energy resources.Our findings suggest that instructors play a crucial role in facilitating effective learning under challenging training conditions.Furthermore,we offer implications for leaders in fire services,who often conduct trainings with their subordinates.展开更多
In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derive...In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.展开更多
This study achieved the construction of earthquake disaster scenarios based on physics-based methods-from fault dynamic rupture to seismic wave propagation-and then population and economic loss estimations.The physics...This study achieved the construction of earthquake disaster scenarios based on physics-based methods-from fault dynamic rupture to seismic wave propagation-and then population and economic loss estimations.The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry,stress field,rock properties,and terrain.Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved.The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment.Additionally,there are significant variations in human losses,even though the economic losses are similar across different scenarios.Dali Bai Autonomous Prefecture,Chuxiong Yi Autonomous Prefecture,Yuxi City,Honghe Hani and Yi Autonomous Prefecture,and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks,with Yuxi and Honghe being particularly vulnerable.Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault.Notably,although the fault is within Yuxi,Honghe is likely to suffer the most severe damage.These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.展开更多
In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk science.During a disaster situation,accurate sensing information is the...In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk science.During a disaster situation,accurate sensing information is the key to efficient recovery efforts.In general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase costs.In this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience.The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional spaces.The method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and availability.The problem formulation,resilience requirements,and application of the optimization algorithm are described in detail.Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement.Both tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by design.The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.展开更多
文摘The objectives of this study were to investigate(1) gender and race differences in mental health and psychological wellbeing among older adults during the COVID-19 pandemic and(2) whether there were significant interaction effects between gender and race. This study used the National Health and Aging Trends Study(NHATS) and COVID-19 public use data files. It focused on mental health and psychological well-being, including loneliness, no time to yourself, poor sleep quality, anxiety, depression, and post-traumatic stress disorder(PTSD). The sample consisted of 2465 older adults, and multivariate logistic and linear regression models were adopted for the analysis. The results show that approximately two out of five older people(43%) experienced poor sleep, followed by loneliness(35%), anxiety(30%), depression(23%), and no time to themselves(11%). The average PTSD score was 11. Mental health and psychological well-being varied by gender and race. There were significant interaction effects on anxiety and depression between females and Black older adults. Black females reported lower levels of anxiety and depressive symptoms than Black males, although the differences were not statistically significant. Black older adults were more likely to experience PTSD but less likely to feel lonely and have poorer sleep than their White counterparts. Female older adults reported poorer mental health and psychological well-being than males, except for Black females. Black females had the lowest levels of anxiety, depression, loneliness, and highest quality of sleep among all females of different ethnic and racial origins.
基金supported by the Institute for Catastrophic Loss Reduction (ICLR)’s quick response research program: 2021 British Columbia’s Fraser Valley Floodingfunding from the Canada Research Chairs Program (Award # CRC-2020-00128)。
文摘Older adults in disaster contexts are often thought of as a passive, vulnerable population that lacks agency and capacities to cope in the aftermath. However, it can be argued that older adults may have underrecognized strengths that can be utilized pre-, peri-, and post-disaster. One of these strengths is older adults' unique social capital that stems from long-standing connections with other members of their respective communities. Using data from in-depth, semistructured interviews with farmers in British Columbia 3–11 months after the 2021 floods, this research explored the experiences of older adult farmers' recovery. The farmers discussed how they leveraged their social capital to aid in their recovery efforts from the flood event. By using their bonding social capital, older adult farmers transformed their existing, deep-rooted connections into post-disaster assistance. This, in turn, generated the idea of the therapeutic community, helping community members cope in the aftermath. This research indicated the need to further examine how older adults in disaster settings can be viewed as assets with community knowledge and skills as opposed to solely as a vulnerable population.
文摘This article presents the results of a study that evaluated VinclesBCN during COVID-19. This digital-based public social service aims to prevent loneliness and isolation in +65-year-old adults living in Barcelona. Through service user(N = 12) and professional(N = 6) interviews and a questionnaire with service users(N = 255), we demonstrate the pivotal role of digital connectedness in transforming VinclesBCN into a lifeline during the pandemic. The analysis revealed the importance of sociability, social support, and, especially, entertainment in coping with pandemic fatigue and facilitating social connectivity and support among users. Users engaged in activities such as sharing images, songs, memes, and daily greetings to provide proximity, sociability, and care among users, whether they belonged to preexisting groups or were newly introduced to the platform. It also facilitated the identification of individuals who needed companionship, comfort, or more specialized support. The findings emphasize the significance of entertainment as a resilience-building strategy during times of uncertainty. Despite the positive impact, not all users equally used the platform. Non-use was strongly associated with being a woman, having a low educational level, having preexisting social relationships, less time of enrolment in the platform, as well as a high perception of loneliness, poor self-reported health, and low mood. The article underscores the need for further research into older adults' digital engagement during crises, its role in building resilience, and advocating for inclusive digital interventions that take into account diverse older adults' needs and experiences in crisis contexts.
文摘The disproportionate risks and impacts of climate change and extreme weather on older adults are increasingly evident. While especially true in disaster-prone areas, human-caused climate change introduces an element of uncertainty even in previously identified “safe” regions such as the Midwestern United States. Using a cumulative disadvantage and vulnerability-informed framework and descriptive statistics from multiple data sources, this article provides an overview of climate impacts, vulnerabilities, and county-level characteristics, focusing on older adults living in Central Ohio. A comparative multiple-case study methodology was used to triangulate regionally representative primary and secondary data sources to examine state and county-level measures of vulnerability, emergency preparedness, and disruptions caused by extreme weather among older adults across eight counties in Central Ohio. Seventy-eight percent of older adults in the sample reported being prepared for emergencies per Federal Emergency Management Agency guidelines. Older adults in Union County reported the highest rates of preparedness, while those in Fayette County reported the lowest. County-level rates of disruption of life activities by extreme weather ranged widely. Among the most rural in the region, Fayette County emerged as uniquely disadvantaged, with the lowest median income, the most vulnerable across multiple social vulnerability dimensions, and the most reported disruptions to life activities from extreme weather. County profiles offer a snapshot of existing vulnerabilities, socioeconomic conditions, special needs, preparedness, and current disruptions among older adults in the region and can inform resource mobilization across community and policy contexts.
基金supported in part by funding from the Social Sciences and Humanities Research Council of Canada (SSHRC)。
文摘Shifting demographics—that is, the growing number of older adults in Canada and internationally—and intersecting climatic risks create a complex landscape of aging in place in disaster contexts. Some older adults are vulnerable due to their underlying health conditions and limited physical mobility. Yet they also exhibit resilience in a distinct manner, owing to their wealth of knowledge, wisdom, and ability to navigate adversities. This article elucidates potential avenues for promoting the involvement of older adults in disaster recovery efforts. Our intention is to recognize and foster older adult resilience and mitigate vulnerability by drawing upon insights from the study titled “In the Aftermath of the 2016 Alberta Wildfires: The Role of Social Work Practitioners and Human Service Professionals in Long-Term Disaster Recovery.” Employing a qualitative research approach, that study recruited 51 participants for semistructured interviews and focus groups. A thematic analysis of the collected data unveiled the pivotal roles played by social workers and human service practitioners in facilitating recovery from the 2016 wildfires in the research. Notably, three overarching themes emerged concerning the promotion of older adults' involvement in disaster recovery: fostering supportive relationships, building partnerships and enhancing collaboration, and addressing ongoing challenges. These findings offer valuable insights into how social work practitioners and human service professionals can effectively facilitate the engagement of older adults in disaster recovery initiatives. This article emphasizes the significance of actively promoting the participation of older adults in disaster mitigation efforts, thereby fostering greater resilience within communities.
文摘Older adults are significantly impacted by natural hazards and disasters that are exacerbated by climate change. Understanding their awareness and preparedness is essential for enhancing disaster resilience. This study investigated the attitudes, actions, and recommendations of older adults regarding natural hazards that pose risks in their geographic area—specifically floods, wildfires, and/or earthquakes in Canada. Methods for this study included survey and focus groups with older adults(n = 161 and n = 10, respectively) and other high-risk groups from across Canada, that are vulnerable to these natural hazards. The main findings from this study are that current awareness and preparedness among older adults is low, though stronger perceptions of risks are associated with risks specific to geographic locations where respondents live. Several barriers, such as hazard vulnerability misperceptions, cost-related reasons, and lack of hazard awareness have resulted in low awareness and preparedness among these populations. The two main recommendations arising from this research are:(1) improve awareness and preparedness with tailor-made emergency preparedness materials for older adults;and(2) adopt community-based approaches to disaster preparedness through existing community groups to strengthen social connections with a focus on locally specific hazards. The findings from this research can be applied to other hazards, including heatwaves and pandemics.
基金supported by the National Social Science Fund of China (Grant No. 23CSH035)the Reaching Out Award from the Hong Kong Special Administrative Region Government Scholarship Fund+1 种基金the Additional Top Conference Grantthe Research Student Attachment Programme (RSAP) from The Hong Kong Polytechnic University。
文摘Despite the well-documented impacts of single natural hazards like earthquakes, less is known about the psychological adaptation to multiple natural hazards, particularly in rural areas. This study investigated the associations of multiple natural hazards with depression among Chinese adults. Data were retrieved from the China Family Panel Studies conducted during 2010–2018. With a sample of 11,633 Chinese adults, multilevel logistic regression was employed to examine the relationships between natural hazard exposure and depression in the total sample and different age groups. Overall, experiencing four or more natural hazards was associated with a higher risk of being depressed. Regarding hazard type, the number of hydrometeorological and biological hazards was associated with a higher likelihood of depression, whereas the number of geologic and other hazards was related to a lower risk of depression. Middle-aged adults from villages were more likely to be affected by natural hazard exposure than older and younger adults. The findings of this study show that cumulative exposure to natural hazards can generate lasting effects on depressive symptoms, particularly in middle adulthood. The findings also suggest that older adults from rural areas may have accumulated more resilience to mitigate the adverse well-being effects of hazard events. Policies and interventions should enhance disaster awareness and preparation for aging residents from multi-hazard communities.
基金the Pierre Elliot Trudeau Foundationthe Vanier Canada Graduate Scholarship for their generous support in funding his doctoral program。
文摘During times of crisis, including pandemics, climate change, and forced migration, much of the discourse in ageing research and intervention centers on the vulnerabilities of older adults. Unfortunately, the valuable contributions of older adults to post-disaster recovery and healing are often overlooked and undervalued. Our aim in this scoping review is to shed light on the critical contributions of older forced migrants to post-migration recovery. We set the scene by introducing the two significant global demographic changes of the twenty-first century: forced migration and ageing. We provide a discourse on older forced migrants, ageing in situations of forced migration, and some of the challenges faced by older forced migrants. We then present some of the substantial roles of older forced migrants in post-migration recovery, including building resilience, contributing to culture and language transfer, providing emotional support, offering mentorship and leadership, participating in community building, and fostering social integration. We close by highlighting some of the lessons that can be drawn from understanding the unique roles played by older adults in post-forced migration recovery and the key actions necessary to promote these roles.
基金supported by the Social Sciences and Humanities Research Council of Canada (SSHRC), Insight Development Grants (Award # 430-2021-00352)funding from the Canada Research Chairs Program (Award # CRC-2020-00128)。
文摘When COVID-19 devastated older-adult organizations(long-term care homes and retirement homes), most public attention was directed toward the older-adult residents rather than their service providers. This was especially true in the case of personal support workers, some of whom are over the age of 55, putting them in two separate categories in the COVID-19 settings:(1) a vulnerable and marginalized group who are disproportionately impacted by COVID-19;and(2) essential healthcare workers. Since the current disaster-driven research, practice, and policy have primarily focused on generalized assumptions that older-adults are a vulnerable, passive, and dependent group rather than recognizing their diversity, expertise, assets, and experiences, this study aimed to identify their contributions from the perspective of older-adult personal support worker(OAPSW). This qualitative study conducted in-depth interviews, inviting 15 OAPSWs from the Greater Toronto Area, Canada. This study uncovered the OAPSWs' contribution at three levels: individual(enhancing physical health, mental health, and overall well-being), work(improving working environment and service and supporting co-workers), and family(protecting their nuclear and extended families). The outcomes inform the older-adult research, practice, policy, public discourse, and education by enhancing the appreciation of older-adults' diverse strengths and promoting their engagement and contributions in disaster settings.
基金funded by the Interreg Central Europe Project“STRENgthening resilience of Cultural Heritage at risk in a changing environment through proactive transnational cooperation–STRENCH,Project index number CE1665”support from the PNRR MUR project ECS_00000033_ECOSISTER。
文摘Vulnerability evaluation plays a key role in risk assessment and reduction and is essential for defining strategies for climate change adaptation and mitigation.In dealing with the safeguarding of cultural heritage at risk,we are still far from adopting and applying an agreed methodology for vulnerability assessment.With the aim to support practitioners,heritage managers,and policy and decision makers to undertake actions that address the protection of cultural heritage at risk,the methodology set up in the framework of the Interreg Central Europe STRENCH is illustrated and discussed here.Based on three major requirements(susceptibility,exposure,and resilience)and a continuous consultation with local stakeholders,the proposed methodology is applicable for evaluating the vulnerability of built heritage and cultural landscape exposed to hydrometeorological hazards,such as heavy rains,floods,and droughts.The results obtained through its validation on 15 case studies from seven Central European regions are shown to underline the strengths and limitations of the methodological approach.Iterative consultation with local stakeholders was fundamental for the definition of the criteria/subcriteria and related values for the assessment of the requirements.Application to further sites in other contexts would surely contribute to strengthening the reliability of the methodological approach.
基金funding support from Oak Ridge Associated Universities(ORAU)Foundations for this research。
文摘Due to a lack of resources,rural communities often face challenges when planning catastrophic events.This project involved applying systems thinking and model-based systems engineering to develop a proof-of-concept,multi-method computer simulation and then determining whether the simulation could be used to assess the efficacy of disaster planning approaches on health outcomes in rural communities,as a function of primary healthcare.The project focus was a rural or non-urban healthcare system experiencing a natural hazard.Both system dynamics and discrete event models were incorporated to represent subsystem operations,crucial disaster responses,as well as three key response systems:public health,emergency management,and healthcare.The subsystem models included several components:policies/procedures,communications,resources,exercises/drills/training,healthcare space and staff,and the flow of affected people into and through the system.The combined simulation can serve as a first step to a more comprehensive approach to helping rural communities achieve more efficient and effective healthcare planning for disaster responses.
基金supported by the Key Laboratory of Mountain Hazards and Earth Surface Processes,Chinese Academy of Sciencesthe European Union’s Horizon 2020 research and innovation program Marie Skłodowska-Curie Actions Research and Innovation Staff Exchange (RISE)under grant agreement (Grant No.778360)+1 种基金the National Natural Science Foundation of China (Grant No.51978533)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No.XDA20030301).
文摘A reliable economic risk map is critical for effective debris-flow mitigation.However,the uncertainties surrounding future scenarios in debris-flow frequency and magnitude restrict its application.To estimate the economic risks caused by future debris flows,a machine learning-based method was proposed to generate an economic risk map by multiplying a debris-flow hazard map and an economic vulnerability map.We selected the Gyirong Zangbo Basin as the study area because frequent severe debris flows impact the area every year.The debris-flow hazard map was developed through the multiplication of the annual probability of spatial impact,temporal probability,and annual susceptibility.We employed a hybrid machine learning model-certainty factor-genetic algorithm-support vector classification-to calculate susceptibilities.Simultaneously,a Poisson model was applied for temporal probabilities,while the determination of annual probability of spatial impact relied on statistical results.Additionally,four major elements at risk were selected for the generation of an economic loss map:roads,vegetation-covered land,residential buildings,and farmland.The economic loss of elements at risk was calculated based on physical vulnerabilities and their economic values.Therefore,we proposed a physical vulnerability matrix for residential buildings,factoring in impact pressure on buildings and their horizontal distance and vertical distance to debrisflow channels.In this context,an ensemble model(XGBoost) was used to predict debris-flow volumes to calculate impact pressures on buildings.The results show that residential buildings occupy 76.7% of the total economic risk,while roadcovered areas contribute approximately 6.85%.Vegetation-covered land and farmland collectively represent 16.45% of the entire risk.These findings can provide a scientific support for the effective mitigation of future debris flows.
基金funded by the NASA Disasters Program grant#NH18ZDA001N001N.
文摘Earth observation(EO) technologies,such as very high-resolution optical satellite data available from Maxar,can enhance economic consequence modeling of disasters by capturing the fine-grained and real-time behavioral responses of businesses and the public.We investigated this unique approach to economic consequence modeling to determine whether crowd-sourced interpretations of EO data can be used to illuminate key economic behavioral responses that could be used for computable general equilibrium modeling of supply chain repercussions and resilience effects.We applied our methodology to the COVID-19 pandemic experience in Los Angeles County,California as a case study.We also proposed a dynamic adjustment approach to account for the changing character of EO through longer-term disasters in the economic modeling context.We found that despite limitations,EO data can increase sectoral and temporal resolution,which leads to significant differences from other data sources in terms of direct and total impact results.The findings from this analytical approach have important implications for economic consequence modeling of disasters,as well as providing useful information to policymakers and emergency managers,whose goal is to reduce disaster costs and to improve economic resilience.
基金supported by the National Natural Science Foundation of China (Grant Nos.42271483,42071364)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (Grant No.KYCX23_1696).
文摘This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.
基金the support of the New Zealand Ministry of Business,Innovation and Employment(MBIE)through the Resilience to Nature’s Challenges 2 for funding this research。
文摘Global and national policy frameworks emphasize the importance of people's participation and volunteers'role in disaster risk reduction.While research has extensively focused on volunteers in disaster response and recovery,less attention has been paid on how organizations involved in disaster risk management can support volunteers in leading and coordinating communitybased disaster risk reduction.In 2019,the New Zealand Red Cross piloted the Good and Ready initiative in Auckland,Aotearoa New Zealand,with the objective to empower local people in resilience building with a focus on volunteers and community participation.This research examined the positive and negative outcomes of Good and Ready and investigated volunteers'experiences in the disaster resilience initiative.It involved the codesign of a questionnaire-based survey using participatory methods with Good and Ready volunteers,the dissemination of the survey to gather volunteers'viewpoints,and a focus group discussion with participatory activities with Red Cross volunteers.The findings highlight that a key challenge lies in finding a balance between a program that provides flexibility to address contextual issues and fosters communities'ownership,versus a prescriptive and standardized approach that leaves little room for creativity and self-initiative.It pinpoints that supporting volunteers with technical training is critical but that soft skills training such as coordinating,communicating,or facilitating activities at the local level are needed.It concludes that the sustainability of Good and Ready requires understanding and meeting volunteers'motivations and expectations and that enhancing partnerships with local emergency management agencies would strengthen the program.
基金supported by the National Science Foundation under Grant Nos.2303578,2303579, 05 27699,0838654,and 1212790by an Early-Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences,Engineering,and Medicine
文摘Facing the escalating effects of climate change,it is critical to improve the prediction and understanding of the hurricane evacuation decisions made by households in order to enhance emergency management.Current studies in this area often have relied on psychology-driven linear models,which frequently exhibited limitations in practice.The present study proposed a novel interpretable machine learning approach to predict household-level evacuation decisions by leveraging easily accessible demographic and resource-related predictors,compared to existing models that mainly rely on psychological factors.An enhanced logistic regression model(that is,an interpretable machine learning approach) was developed for accurate predictions by automatically accounting for nonlinearities and interactions(that is,univariate and bivariate threshold effects).Specifically,nonlinearity and interaction detection were enabled by low-depth decision trees,which offer transparent model structure and robustness.A survey dataset collected in the aftermath of Hurricanes Katrina and Rita,two of the most intense tropical storms of the last two decades,was employed to test the new methodology.The findings show that,when predicting the households’ evacuation decisions,the enhanced logistic regression model outperformed previous linear models in terms of both model fit and predictive capability.This outcome suggests that our proposed methodology could provide a new tool and framework for emergency management authorities to improve the prediction of evacuation traffic demands in a timely and accurate manner.
基金part of the project“FIRE:Feedback Instruments for Rescue Force Education–Leadership and Teamwork in High Risk Environments,”supported with funds from the State of North Rhine-Westphalia,Germany。
文摘Leadership courses in the fire services are highly challenging,and they can seriously exhaust trainees and hamper their selfregulated learning efforts(for example,setting goals,focusing attention,seeking feedback).We theorize that experiences of failure or overload can curtail trainees'available energy resources on subsequent training days,which,in turn,should affect trainees'learning efforts.Given instructors'central role in leadership courses,we hypothesize that supportive and humble instructor behaviors decrease experiences of failure and overload and,thus,increase self-regulated learning.Moreover,we argue that supportive instructor behavior may amplify the positive effects of high energy resources,while humble behavior may alleviate the negative impact of low resource levels.We tested preregistered hypotheses with 118 firefighters participating in two-week leadership courses at a German fire academy.The participants completed short web-based questionnaires before and after classes each day.Multilevel analyses confirmed that perceived daily supportive and humble instructor behavior predicted trainees'reports of daily self-regulated learning activity.Notably,this effect was independent from positive effects of trainees'reported energy resources in the morning.Supportive and humble behavior did not moderate the eff ect of energy resources.Our findings suggest that instructors play a crucial role in facilitating effective learning under challenging training conditions.Furthermore,we offer implications for leaders in fire services,who often conduct trainings with their subordinates.
基金funded by the Engineering&Physical Sciences Research Council(EPSRC)Impact Acceleration Account Award EP/R511742/1。
文摘In this study,a broad range of supervised machine learning and parametric statistical,geospatial,and non-geospatial models were applied to model both aggregated observed impact estimate data and satellite image-derived geolocated building damage data for earthquakes,via regression-and classification-based models,respectively.For the aggregated observational data,models were ranked via predictive performance of mortality,population displacement,building damage,and building destruction for 375 observations across 161 earthquakes in 61 countries.For the satellite image-derived data,models were ranked via classification performance(damaged/unaff ected)of 369,813 geolocated buildings for 26 earthquakes in 15 countries.Grouped k-fold,3-repeat cross validation was used to ensure out-of-sample predictive performance.Feature importance of several variables used as proxies for vulnerability to disasters indicates covariate utility.The 2023 Türkiye-Syria earthquake event was used to explore model limitations for extreme events.However,applying the AdaBoost model on the 27,032 held-out buildings of the 2023 Türkiye-Syria earthquake event,predictions had an AUC of 0.93.Therefore,without any geospatial,building-specific,or direct satellite image information,this model accurately classified building damage,with significantly improved performance over satellite image trained models found in the literature.
基金supported by the Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology (2022B1212010002)Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0203)the Shenzhen Science and Technology Program (KQTD20170810111725321)
文摘This study achieved the construction of earthquake disaster scenarios based on physics-based methods-from fault dynamic rupture to seismic wave propagation-and then population and economic loss estimations.The physics-based dynamic rupture and strong ground motion simulations can fully consider the three-dimensional complexity of physical parameters such as fault geometry,stress field,rock properties,and terrain.Quantitative analysis of multiple seismic disaster scenarios along the Qujiang Fault in western Yunnan Province in southwestern China based on different nucleation locations was achieved.The results indicate that the northwestern segment of the Qujiang Fault is expected to experience significantly higher levels of damage compared to the southeastern segment.Additionally,there are significant variations in human losses,even though the economic losses are similar across different scenarios.Dali Bai Autonomous Prefecture,Chuxiong Yi Autonomous Prefecture,Yuxi City,Honghe Hani and Yi Autonomous Prefecture,and Wenshan Zhuang and Miao Autonomous Prefecture were identified as at medium to high seismic risks,with Yuxi and Honghe being particularly vulnerable.Implementing targeted earthquake prevention measures in Yuxi and Honghe will significantly mitigate the potential risks posed by the Qujiang Fault.Notably,although the fault is within Yuxi,Honghe is likely to suffer the most severe damage.These findings emphasize the importance of considering rupture directivity and its influence on ground motion distribution when assessing seismic risk.
基金funded by the Integrating Energy and Computing Networks project funded through the USACE Military Programs
文摘In recent years,the notion of resilience has been developed and applied in many technical areas,becoming exceptionally pertinent to disaster risk science.During a disaster situation,accurate sensing information is the key to efficient recovery efforts.In general,resilience aims to minimize the impact of disruptions to systems through the fast recovery of critical functionality,but resilient design may require redundancy and could increase costs.In this article,we describe a method based on binary linear programming for sensor network design balancing efficiency with resilience.The application of the developed framework is demonstrated for the case of interior building surveillance utilizing infrared sensors in both twoand three-dimensional spaces.The method provides optimal sensor placement,taking into account critical functionality and a desired level of resilience and considering sensor type and availability.The problem formulation,resilience requirements,and application of the optimization algorithm are described in detail.Analysis of sensor locations with and without resilience requirements shows that resilient configuration requires redundancy in number of sensors and their intelligent placement.Both tasks are successfully solved by the described method,which can be applied to strengthen the resilience of sensor networks by design.The proposed methodology is suitable for large-scale optimization problems with many sensors and extensive coverage areas.
