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The LRAD, the brand name of Genasys' industry-leading acoustic hailing devices (AHDs) product range, has been discussed in the context of the weaponisation of sound. This article contributes a critical science and technology studies perspective by approaching AHDs as security sound technologies: as products within a security and disaster audio industry, that partake in, and are symptomatic of, a broader logic of securitisation. The goal here is neither decrying AHDs as invested in injurious sound nor making distinctions between purportedly violent and benign AHD features and applications, but to inquire into "the concept of an AHD" and, in turn, to understand the logics and workingsthat underlay AHDs' framing and adoption as 21st-century security sound solutions across the military-civilian, government-corporate continuum. While there are important differences between AHD models, all AHDs produce narrow and directional acoustic beams and broadcast high-intensity, far-reaching "unmistakable" voice commands and piercing deterrent tones. What makes AHDs "sound" and otherwise desirable as "workhorses" for a broad range of security and safety applications? The article examines different capability gaps these devices avowedly "bridge" and define. In particular, it discusses AHDs' humanitarian violence, disaster communications and relief, crowd control, and perimeter security applications and functions within US contexts. [ABSTRACT FROM AUTHOR]

In disaster rescue, multiple resource allocation strategies before and after disasters are a key research issue. Response planning for sudden-onset disasters must take into account the inherent uncertainties associated with the catastrophe and potential ensuing disasters. This article investigates the problem of resource allocation optimization in the context of natural disasters accompanied by the occurrence of multiple secondary hazards. A two-stage stochastic optimization model is proposed to simulate the scenario of random occurrence and severity of multiple natural hazards to optimize the resource allocation of rescue teams, warehousing items and medical resources when multiple natural hazards occur in combination. A particle swarm optimization (PSO) algorithm is designed and compared with a commercial solver (CPLEX) and lower bound at various scales. The results show that PSO performs optimally for large-scale cases. A case study, undertaken in Beijing, demonstrates that this method can improve the estimated casualty rescue success rate to 90.6%. [ABSTRACT FROM AUTHOR]

The effective distribution of relief goods is critical in mitigating the impact of natural disasters and preserving human life. This study addresses a relief goods distribution problem, assuming the existence of multiple relief orders that must be delivered to various disaster-stricken regions from a network of warehouses using a fleet of diverse vehicles. The objective is to identify the most suitable warehouse for each relief order, allocate relief orders to vehicles, batch the orders in the designated vehicles, and devise routing plans to minimize the total delivery time. A mixed-integer linear programming model is formulated to tackle this problem. Owing to the problem's NP-hard nature, a metaheuristic algorithm, known as the Multiple League Championship Algorithm, is developed. Furthermore, two innovative variants of the MLCA, namely the League Base Multiple League Championship Algorithm (L-MLCA) and the Playoff Multiple League Championship Algorithm (P-MLCA), are introduced. Experimental results indicate that the P-MLCA outperforms the other two algorithms. The solutions derived from the P-MLCA are compared with the optimal solutions obtained by a commercial solver for small-scale problems. This comparative analysis demonstrates the promising performance of the P-MLCA in finding the optimal distribution of relief goods. [ABSTRACT FROM AUTHOR]

Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives. [ABSTRACT FROM AUTHOR]

Keywords: Constraints; disasters; hurricanes; liminality; social media; uncertainty EN Constraints disasters hurricanes liminality social media uncertainty 300 321 22 05/16/23 20231101 NES 231101 1. Past hurricane experiences were less useful than they would have been for a more typical hurricane, as many noted in their social media posts, e.g., " ... We've had hurricanes before, but not like this one - so I don't know at all what to expect, really." The monologic series of social media posts enable study of those affected over the duration of the hazard and better understand social media expression especially from voices that are otherwise overcome by the highly public and therefore "louder" experiences of disaster. First, to isolate the people who might have experienced both flash flood and tornado hazards during Hurricane Florence, we collected all tweets from 8 September to 20 September 2018 containing both a tornado-related keyword ("tornado", "funnel cloud") and a flash flood-related keyword ("flood", "flash flood", "storm surge"), including keywords as hashtags (e.g., "#flood"). [Extracted from the article]

The book "Catastrophes, confrontations, and constraints: how disasters shape the dynamics of armed conflict" by Tobias Ide explores the interplay between natural disasters and armed conflicts. The author examines how disasters can impact conflict intensity and duration, proposing three causal mechanisms: impact on motivations, impact on the strategic context, and impact on communication. The book uses qualitative comparative analysis to identify complex patterns and provides insights into the diverse impacts of disasters on armed conflict dynamics. It also highlights the potential for disasters to act as catalysts for peace and environmental stewardship, suggesting opportunities for integrating peacebuilding and conservation efforts. Overall, the book contributes to the understanding of conflict dynamics and the interaction between environmental disasters and socio-political factors. [Extracted from the article]

India has been identified as one of the most vulnerable countries within the global climate crisis. Kerala has been highlighted as a hotspot of climate vulnerability within India, and this was illustrated by the floods of 2018. Nearly, 500 people lost their lives during the floods and 1.4 million were displaced, with the reconstruction programme still taking place today. Anecdotal andmedia accounts have suggested that the Keralan diaspora played a significant part in the disaster relief and reconstruction effort both during and after the floods. However, there has been very little detailed empirical investigation into this particular diasporic engagement during a time of crisis. Drawing upon original empirical research, this paper will examine the nature and impact of Keralan diasporic engagement during, and after, the 2018 floods, particularly focussing upon whether this was an inclusive relief and development effort across the categories of caste, region and religion within the state. [ABSTRACT FROM AUTHOR]

Following Hurricane Katrina, tropes of disaster relief and recovery were evoked to legitimate swift modifications to public policy in New Orleans. It is well-documented that such policy changes led to increased privatization and gentrification, but also notable is the increase in surveillance that emerged under a discourse of public safety. This research utilizes situational analysis (SA), a critical qualitative method, to examine through the lens of surveillance capitalism, a range of interconnected conditions that shape surveillance strategies within the New Orleans' tourism complex. Results show the ways in which safety, identity and opportunity discourse is used to support increased surveillance measures and delineates for whom a tourism destination is to be secured and who is to be surveilled. [ABSTRACT FROM AUTHOR]

Using data sets from ethnographic research, this article examines how language minorities navigate textual regimes in disaster recovery procedures governed by bureaucratic recovery technologies. To discuss the impacts of Western climate governance regimes and alternative disaster recovery communication, this article traces rhetorical practices of transnational multilingual communities of color around a disaster relief program. I argue that community-engaged translation practices operate as the locus of rhetorical strategies against disaster recovery injustice. [ABSTRACT FROM AUTHOR]

Recovery from a disaster is always a difficult process. But what happens when a second disaster befalls the library soon after the recovery from one is completed? In February and May 2020, the Ensor Learning Resource Center suffered this fate. Because of deferred maintenance and lack of oversight, large scale leaks caused major damage to the same area of the building twice in three months. Recovering from a disaster, especially as part of a larger organization, is never easy with pitfalls aplenty, but it can be done if you are patient and work within (and sometimes, without) your organization. This column focuses on efforts, communication & leadership strategies, and provides advice for those who may ever find themselves in this situation. [ABSTRACT FROM AUTHOR]

Building and road detection from the high-resolution remote sensing images has many applications in a wide range of areas including urban design, real-estate management, and disaster relief. Extracting buildings and roads from the remote sensing images have been performed by human experts manually, but it takes too much time and the cost of labor to build is also very high. So, an automated system that can emulate a human operator is desired. Our goal is to develop a system for automatically detecting buildings and roads directly from high-resolution satellite images. Therefore, we propose Internal Gray Variance (IGV) to detect the buildings and roads in the urban areas. First, the satellite images are enhanced by using morphological operators, which enhance the edges of the objects. Then multiseed-based clustering technique detects the building and road edges using the variance in the gray levels. To reduce the false alarm, tiny regions that are improbable to be buildings and roads are separated. Finally, by using the adaptive threshold-based segmentation technique, the buildings are segmented and the road network is extracted based on supervised directional homogeneity. Finally, we evaluate our system on a large-scale road and building detection data sets that are publicly available. [ABSTRACT FROM AUTHOR]

After an earthquake, a track-bridge system may suffer damage and residual deformation, posing a threat for trains passing over a bridge. However, since railway transportation is an important channel for disaster relief, the timely passage of trains is of great significance for the transportation of materials and personnel. In the past, subjective and experience-based judgments were made on whether a train can pass over an earthquake-damaged bridge, and no relevant numerical calculations were carried out. Therefore, a strategy for the evaluation of train performance when passing over existing bridges after earthquakes was proposed. The strategy considers the randomness of track irregularity, and evaluates the running safety performance (RSP) and running comfort performance (RCP) of trains passing over existing bridges from the perspective of probability. The probability law of a lateral residual bridge deformation influence on the RSP and RCP after an earthquake was revealed. Finally, the maximum train passing speed which ensures the RSP was recommended. [ABSTRACT FROM AUTHOR]

As for relationships with political media minders, it is like a "pressure-cooker" (p. 168) in that the power of political media minders, fully aware of the political importance of media reporting in disasters, becomes increasingly blurred but huge enough to intervene in or even undermine disaster managers' professionalism. They share good experiences, like taking advantage of disaster anniversaries to educate politicians about disaster management, leadership, and communication, and seizing the right time and place to discuss business with political actors. The book takes multiple actors in crisis and disaster communication into consideration, including politicians, front-line operational staff, political media minders as well as emergency managers. [Extracted from the article]

Land change science co-evolves with remote sensing technology. The world has witnessed an exponential growth in Earth observation satellites since 1972, and concurrently, land change research has experienced transformative advancement. This review summarizes the major milestones in global land cover and change mapping in a chronological order, from the pioneering efforts in the 1980s to the latest innovations at present, illustrating the tremendous progress in monitoring global land change from space. The second part of the review presents a critical synopsis of the recent progress in land change research, focusing on the technical aspects of temporal trends characterization, change mapping and area estimation, as well as the applied aspects of driver attribution and the complex consequences to the Earth system and human society. The last part of the article offers insights in the strategic directions of land change monitoring, including generation of analysis ready data, application of artificial intelligence algorithms, reconstruction of historical land change records, and near-real-time land change monitoring. Land change science will continue to play a vital role in addressing a wide range of global challenges, including climate change and carbon sequestration, food security, sustainable energy transition, natural disaster relief and environmental change in conflicted societies. [ABSTRACT FROM AUTHOR]

Natural disasters are increasing globally. Geographically, the Indo-Pacific region is the world's most vulnerable region and navies here have been called upon frequently to provide humanitarian assistance. Thus, humanitarian assistance and disaster relief (HA/DR) missions have emerged as a key role for the regional navies. However, recent studies in disaster management indicate that disaster risk mitigation needs a greater focus on development issues and preparedness than post-disaster humanitarian response. This paper posits that navies too must expand their focus beyond HA/DR to contribute to other aspects of disaster management and promote collaboration with other stakeholders which could help overall to mitigate risks from natural disasters. [ABSTRACT FROM AUTHOR]

Hurricanes can cause severe property damage and casualties in coastal regions. Diesel fuel plays a crucial role in hurricane disaster relief. It is important to optimize fuel supply chain operations so that emergency diesel fuel demand for power generation in a hurricane's immediate aftermath can be mitigated. It can be challenging to estimate diesel fuel demand and make informed decisions in the distribution process, accounting for the hurricane's path and severity. We develop predictive and prescriptive models to guide diesel fuel supply chain operations for hurricane disaster relief. We estimate diesel fuel demand from historical weather forecasts and power outage data. This predictive model feeds a prescriptive stochastic programming model implemented in a rolling-horizon fashion to dispatch tank trucks. This data-driven optimization tool provides a framework for decision support in preparation for approaching hurricanes, and our numerical results provide insights regarding key aspects of operations. [ABSTRACT FROM AUTHOR]

This article presents the results of the five waves of the National Income Dynamics Study – Coronavirus Rapid Mobile Survey (NIDS-CRAM) on food security between May 2020 and May 2021. Despite significant investments in social protection, food insecurity and household and child hunger remained stubbornly high. We conclude that given the protracted nature of the pandemic, slow economic recovery, household and child hunger have stabilised at higher levels than before the pandemic. The phasing out of emergency relief coupled with the constrained economic situation, are some of the reasons why levels of food insecurity and hunger are likely to remain high in the near future. Strict lockdown regulations also reduced employment and income from informal economic activities. Social support for vulnerable individuals and households remain an urgent priority. Continuing support targeted at households with children is particularly important given the dire consequences of enduring hunger for stunting, and on children's long-term development. [ABSTRACT FROM AUTHOR]

Disaster assistance in the United States has faced criticism for widening the unequal impacts of disasters, but little is known about whether and how this phenomenon applies to businesses. Small businesses make up most businesses in the United States, but they are particularly vulnerable to hazards given their relative lack of capital. Because recovery assistance to businesses is primarily loan based, this lack of capital can create conflicts in how aid is perceived and allocated. Assistance providers must balance aiding the most severely damaged businesses and lending to those that will be able to repay; for small business, the threat of additional debt can make even low-interest loans seem risky. With this research I attempted to better understand how these competing factors play out in recovery through regression analyses of approved loan amounts and loan utilization decisions in Galveston (TX) after Hurricane Ike. I found that businesses with higher repayment ability such as larger businesses, older businesses, and corporations were approved for high loan amounts. Smaller businesses, businesses with higher damage, and businesses with longer loan terms were less likely to use the loans in their recovery, despite being approved. These findings suggest that businesses with the resources to recover were more likely to be the ones benefiting from additional disaster assistance. These findings suggest that planners may need to create their own recovery programs specifically targeting subgroups of businesses that are important to their communities. Although important to many economic development initiatives, very small businesses, entrepreneurs, and sole proprietors may not benefit from federal assistance, particularly if they were severely damaged. [ABSTRACT FROM AUTHOR]

Information diffusion on social media during disasters is an important indicator of community resilience. As a common natural hazard in the U.S., winter storms often cause adverse socio-economic impacts on human society. Understanding people's perception and behaviours during winter storms is important to mitigate negative impacts and promote community resilience. This study applies text mining and spatial analysis methods on Twitter data during Winter Storm Diego on 2018 December. Different from previous studies focusing on original tweets, this study utilized retweets to model information diffusion in the contiguous United States and analysed the geographic distribution of information flows in various topics. The diffusion extent and direction of the storm-related retweets were compared among different topics. Kernel density maps and standard deviational ellipse were applied to model the spatial distribution of the retweets in different topics. The result shows that people outside of the affected areas expressed more negative sentiment towards the storm than people in the affected areas. Also, distance decay of retweet density has been found and the decay rate differs in different topics. These findings of the analyses will provide support for disaster relief, information communication and broadcasting through social media platforms. [ABSTRACT FROM AUTHOR]

Transferring the Dutch Room for the River (RftR) to other jurisdictions is not just about changes in technology. It also requires a fundamental shift in culture and governance, and hence is challenging to implement. Through a qualitative case study and applying a frame/framing analytical lens, we examine how the Dutch RftR was perceived and implemented in southern Alberta, Canada in three projects. Overall, Alberta interviewees perceived the RftR projects as triggering a paradigm shift from 'fighting water' to 'living with water' and as opening opportunities to expand this approach into future flood risk management projects across the province. But did this support translate into policies and practices? We explore the extent to which Alberta adopted four features of the RftR approach: (a) shifting away from mega-infrastructure, (b) making space for rivers, (c) moving people, and (d) regulating floodplain development. Also drawing on policy adoption models, we assess whether the RftR approach resulted in a significant shift towards more preventative, environmentally sustainable flood mitigation to reduce disaster risks and damages or whether a business-as-usual approach of undeterred development in flood-prone areas was undertaken with resulting reliance on disaster relief programmes. [ABSTRACT FROM AUTHOR]

Multiple pipeline failures in water distribution systems (WDS) caused by natural hazards such as earthquakes can significantly reduce the functionality of the systems. After a natural disaster, a quick recovery of WDS is desirable to ensure that critical facilities have access to clean water. However, experience from recent disasters has shown that the recovery period for WDS can be long. The recovery period depends on various factors, such as available resources, budget, etc. As such, all the failed pipelines cannot be repaired at the same time. Hence, utilities need to prioritise damaged pipelines and perform the repair in such a way that the functionality of the WDS is optimised. Post-event system functionality gain may vary significantly depending on the choice of sequential recovery paths. Unlike other infrastructures, the functionality of WDS is not only dependent on topological integrity but also on hydraulic performance. In this study, a post-disaster recovery planning model for WDS is proposed. The restoration process is modelled in terms of topological efficiency, hydraulic resilience and hydraulic availability. The optimal recovery sequence is determined based on the recovery effectiveness estimated by the area under each recovery sequence curves. The proposed method is illustrated for a moderate size WDS. [ABSTRACT FROM AUTHOR]

For tourism-dependent communities, disaster recovery preparedness is essential for the retail and hospitality industries because they are often under the greatest threat of natural disasters. Using the National Disaster Recovery Framework, this study focuses on understanding natural disaster preparedness and recovery for small businesses within the tourism community of Gatlinburg, Tennessee. Researchers utilized an exploratory qualitative approach and conducted interviews via referrals with small business owners/managers (n = 11). Findings revealed a general, yet critical, lack of disaster preparedness that inhibited a swift and timely business recovery. Fundamental issues point to the need to prepare for unexpected disasters to aid recovery. [ABSTRACT FROM AUTHOR]

ASEAN member states have invested substantially in cooperation on humanitarian assistance and disaster relief (HADR). Despite broad support for the idea of 'localizing' HADR governance, the rise of regional agency has in practice led to uncertainty and frictions between humanitarian stakeholders. The article makes sense of these tensions by investigating the narratives through which intra- and extraregional agents construct the role of the ASEAN Coordinating Centre for Humanitarian Assistance on Disaster Management (AHA Centre). Based on the assumption that narratives are central legitimating practices when new agents enter a governance arena, it analyzes textual material produced by different humanitarian organizations that operate in Southeast Asia, as well as interviews with representatives from these organizations. Their accounts of the AHA Centre's role can be grouped into four narratives that are bound up with competing ideas about regional humanitarian order: an affirmative one, a skeptical one, a critical one and a transformative one. The article thus rejects characterizations of regional HADR as a rationally designed 'architecture' and instead defines it as a deeply political arena where different conceptions of order are asserted, contested and negotiated. [ABSTRACT FROM AUTHOR]

As one of the most severe natural disasters in the world, floods caused substantial economic losses and casualties every year. Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief. Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar (SAR) images and deep learning methods is relatively low, this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network (FWENet) for flood information extraction. Then three classical semantic segmentation models (UNet, Deeplab v3 and UNet++) and two traditional water body extraction methods (Otsu global thresholding method and Object-Oriented method) were compared with the FWENet model. Furthermore, this paper analyzed the water body area change situations of Poyang Lake. The main results of this paper were as follows: Compared with other five water body extraction methods, the FWENet model achieved the highest water body extraction accuracy, its F1 score and mean intersection over union (mIoU) were 0.9871 and 0.9808, respectively. This study could guarantee the subsequent research on flood extraction based on SAR images. [ABSTRACT FROM AUTHOR]

In order to improve the effect of agricultural disaster management, this paper uses environmental big data technology to analyse historical agricultural disaster data and analyse the role and impact of this technology in agricultural disaster management. How to efficiently use the effective resources in the meteorological big data to optimise the mining effect is an important issue in the assessment of agrometeorological disasters. Parallel information entropy attribute reduction algorithm can effectively reduce the scale of research data and increase the rate of data preprocessing. The classification assessment and early warning of agrometeorological disasters involve data mining classification prediction technology. Unlike most classification prediction algorithms, KNN classification is particularly suitable for application to meteorological data sets with cross-class domains and can accelerate the classification and prediction speed of big data in the parallel processing architecture. Finally, this paper designs experiments to verify the method proposed in this paper. The results of data analysis show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

We formulate an integrated mathematical model for distribution of the injured on a three-type transportation network with destroyable independent links and with actual travel times considering two different groups of the injured. The aim is to find the temporary locations for aid stations and their capacities, the percentage of the injured with various severities allocated to each station and to different routes and the number of vehicles so that the total relief time is minimized. For this, we present a new approach, called circle-based approach, in which the effects of the disaster are considered as a number of concentric circular regions. The original problem is formulated as an integer nonlinear mathematical model. To solve large problems, two algorithms, an evolutionary algorithm based on simulated annealing and a discrete version of the imperialist competitive algorithm, are developed. An empirical study of the earthquakes in the city of Tehran, Iran, is conducted. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic magnified the cracks and chasms in our social structures. It threw a hard truth into sharp relief: in an emergency response, it is the marginalized members of our community who are most at risk of being left behind. This article aims to contribute to the COVID-19 dialogue series by highlighting the social justice and social equity concerns related to disability justice in an emergency response. Specifically, we examine the case of K-12 school closures in Washington State in response to the COVID-19 public health crisis, for its impact on students with disabilities. We find that the response to COVID-19 emergency guided by utilitarian approaches, led to further marginalization and inequitable impact on special education students and their families. We propose that an alternative approach of Targeted Universalism that involves universal goal-setting and targeted strategy development, when modified to include Restorative and Transformative justice practices, may be a valuable tool to create the conditions for an emergency response that promotes social equity and social justice. [ABSTRACT FROM AUTHOR]

Though coastal environments are recognized as important community resources, the post-disaster reconstruction phase, an artificial-oriented strategy for human security and coastal development, may degrade coastal landscapes and avert pro-environmental behaviors. However, the impact of these outcomes has been studied to a limited extent only. This study aimed to explore residents' pro-environmental behavior, green performance, and motivation for coastal green infrastructure (CGI) through the difference in high-low green self-efficacy (GSE). Survey data was used for a small rural community in Japan, where residents started the Camellia Forest Project as CGI after the 2011 Great East Japan Earthquake and Tsunami (GEJET) under coastal artificial development. To examine the difference in high-low-GSE by participants' characteristics, environmental behaviors, and awareness, odds ratios (ORs) were calculated. The results showed that residents' characteristics – men, retirement age, second-generation household type, and mowing experience – were positively associated with high-GSE. People having high-GSE showed more effects on their coastal green management, Eco-DRR (OR: 6.04, 95% CI, 2.02–18.04), and social cohesion (OR: 6.27; 95% CI, 0.97–14.38) than those with low GSE in the post-disaster recovery process. This study suggests that GSE offers a base to nurture residents' commitment to a coastal green environmental restoration under CGI development. [ABSTRACT FROM AUTHOR]

The Wide Field of View (WFV) imaging system equipped on the GaoFen-6 (GF-6) optical remote sensing satellite can acquire an image with a swath width 800 km and a resolution of 16 m, which is the largest Earth observation swath width among similar satellites in the world. With the advantages of the high spatial resolution and the wide field of view, GF-6 WFV images are widely used in agricultural resources monitoring, forestry resources investigation, and disaster relief. However, the existence of clouds is inevitable problem in GF-6 WFV images, which influences their availability. To quickly and accurately detect cloud areas in GF-6 WFV images, a cloud detection method for GF-6 WFV images based on the spectrum and variance of superpixels is proposed in the paper. First, the GF-6 WFV image is down-sampled. The simple linear iterative clustering algorithm is used to segment down-sampled images to obtain superpixels. The initial cloud detection result is obtained based on the spectrum of superpixels. Second, the initial cloud detection result is refined based on the variance of superpixels to eliminate the influence of cloud-like ground objects. Finally, the refined cloud detection result is post-processed using the region growing algorithm and expansion algorithm. The post-processed cloud detection result is up-sampled to obtain cloud detection result of the GF-6 WFV image. The experimental results show that the recall and precision of the proposed method are 84.61% and 88.46%, respectively, providing good cloud detection results for GF-6 WFV images. [ABSTRACT FROM AUTHOR]

In this study, a three-level relief chain problem is proposed in pre- and postdisaster phases in order to support sustainability in the context of humanitarian relief operations. We develop a comprehensive relief logistics network considering the strategic and tactical planning issues of facility location, procurement, and resource allocation, and including multi-objective mixed-integer stochastic programing model which minimizes total costs of the humanitarian relief supply chain (the costs of the preparedness and response phases), maximizes the social welfare (through maximizing the minimum satisfaction rates among the disaster areas), and minimizes the environmental impacts (ecological impacts of the packaging of relief goods, and CO2 emissions in transportation routes of the proposed network in pre- and postdisaster phases), simultaneously. The proposed multi-objective sustainable humanitarian relief logistics problem (SHRLP) is solved using the Compromise Programing (CP) technique and Lexicographic Optimization Method (LOM). Two numerical examples with different scales are provided based on surveying data of possible Tehran earthquakes. The results of solving numerical examples via CP and LOM methods are presented and compared. The efficient Pareto optimal solutions are also provided to examine different preferences of the objectives. LOM produces better solutions than the CP technique in terms of the satisfaction rates and fair distribution of relief items. Also, the results indicate that the novel model is able to survey and model both the sustainability approach and the uncertainty of a disaster relief condition in support of more efficient and effective plans for sustainable facility locations, and procurement and delivery of reliefs. [ABSTRACT FROM AUTHOR]

We consider a population model in which individuals arrive as per the batch Markovian arrival process. The population is influenced by catastrophes which occur according to Markovian arrival process and it eliminates each individual of the population in a sequential order with probability p until the one individual survives or the entire population is annihilated. We first obtain the steady-state vector generating function of the population size distribution at arbitrary epoch and then the distribution is extracted in term of roots of the associated characteristic equation. Further, we obtain the population size distribution at post-catastrophe and pre-arrival epochs. Finally, a few numerical results are given. [ABSTRACT FROM AUTHOR]

Business recovery after a disaster plays an important role in the socioeconomic recovery of a community. This study focuses on the development of a probabilistic modelling approach for quantifying and predicting business recovery through Bayesian linear regression. The proposed modelling approach consists of three steps including data collection, development of model forms, and model selection through rigorous evaluation and elimination steps. Four attributes, namely business cease operation days, revenue recovery, customer retention, and employee retention, which describe the post-disaster recovery state of a business, are considered. One of the main contributions of this study is incorporating the interplay between household and businesses in a community in developing predictive business recovery models. Towards that direction, different methods to account for the effect of household recovery into the customer retention rate of a business are investigated and proposed. As an application, the proposed modelling approach is applied on the results of a longitudinal field study at the community of Lumberton, NC, which was heavily impacted by the 2016 Hurricane Matthew, focusing on business recovery. The predictive models proposed in this study may be further applicable in risk-based resilience assessment of communities following disastrous events. [ABSTRACT FROM AUTHOR]

In this paper, we will highlight the correspondences between the Socially Engaged Buddhism movement, especially as defined in the practice of the late Thich Nhat Hanh, and the core principles of humanity, impartiality, neutrality and independence originally adopted by the International Red Cross and Red Crescent Movement. These principles also underpin the neutral, impartial and independent approach to humanitarian action, used by agencies working under the auspices of the United Nations' Inter-Agency Standing Committee and Office for the Coordination of Humanitarian Affairs, along with those who have signed the Code of Conduct for the International Red Cross and Red Crescent Movement and Non-Governmental Organizations in Disaster Relief. We hope this paper is a modest but useful contribution to create better understanding and to generate dialogue among different stakeholders in the humanitarian field, particularly in the context of armed conflict. [ABSTRACT FROM AUTHOR]

In the real world, secondary disasters occur frequently after primary disasters, and their diverse and uncertain nature along with the destruction may make the emergency relief operations more challenging. A scenario-based three-stage stochastic programming model is proposed considering the correlation between primary and secondary disasters under uncertain conditions. In order to enhance the computational tractability of the model, an accelerated Benders decomposition algorithm is formulated. In addition, to tackle large-scale cases, an approximation method employing the worst-case scenario in the third stage is established to improve the computational tractability. A computational study is performed to highlight the significance of the model and the efficiency of the proposed solution strategy. The results indicate that, by considering secondary disasters, demand satisfaction can be considerably improved compared with considering only primary disasters. [ABSTRACT FROM AUTHOR]

The United Nations has consistently supported the elimination of nuclear weapons, frequently referring to the effects of the 1945 bombings of Hiroshima and Nagasaki, Japan. Despite awareness of the severe effects of the use of nuclear weapons on targeted or adjacent populations, at a practical level the UN system has not specifically planned how it would respond to assist the victims of nuclear weapons. Moreover, the current humanitarian system is largely unprepared for the special challenges of such an event. This is significant for three reasons: First, the probability of the explosion of a nuclear weapon in populated areas may be low, but such an event is not impossible; there are many ways nuclear weapons might be detonated, either deliberately or inadvertently, with severe humanitarian consequences. Second, in assisting the victims, the UN-coordinated humanitarian system could put its own personnel at risk. Third, although any humanitarian response would at least in part be only palliative, and prevention remains the only adequate response, organizations within the Red Cross and Red Crescent Movement have already begun considering these issues and the UN needs to be ready to work with them. There are many ways in which the humanitarian system could better prepare for the challenges that even a single nuclear explosion would pose, and such preparation is consistent with the UN’s mission and the secretary-general’s recent call for all feasible steps to be taken to avoid systemic humanitarian failures. [ABSTRACT FROM PUBLISHER]

The article presents a case study examining the responses of librarians to the 2011 Great East Japan Earthquake and Fukushima Daiichi nuclear power plant disaster. Topics address include a poster created by a librarian from Fukushima, Japan that documented librarians efforts to provide reading and information services despite damages to library buildings, as well as the use of libraries as shelters.

The use of information technology during the COVID-19 pandemic raises significant questions around the protection of personal data in a disaster. This paper considers how the clear benefits of using and sharing such data in disaster scenarios can be achieved while recognising an individual's right to privacy through examining the experiences of Taiwan and New Zealand. These states represent two successful COVID-19 response strategies which utilised different approaches to the use of technology. In Taiwan, the response made significant use of personal data and information technology. New Zealand, by contrast, has relied upon stringent lockdowns and extreme limits upon personal freedoms. The paper considers the different approaches to data and privacy that underpinned these responses and considers whether New Zealand can learn from the Taiwanese experience in future disaster planning. In doing so, the paper concludes by examining the wider question of when and if the community's expectation of a safe environment should trump the rights of individuals to retain personal data both in the context of pandemics and in other emergency or disaster scenarios. It also raises deeper questions, exposed by the COVID-19 response, about whether our current approach to privacy is sustainable in the digital age. [ABSTRACT FROM AUTHOR]

The earthquakes that struck Christchurch, New Zealand, in 2010–11 ruptured both the material and sociocultural architectures of the city. Subsequent examinations of the landscape have explored the alternative imaginations and narrations of urban space that have taken hold during recovery. Focusing on the prolific presence of transitional organisations in the city-centre, some of these have argued that the alternative subjectivities and places that arose post-quake represent responses that challenge and contest state expectations of what constitutes 'recovery'. This paper develops these ideas further, arguing that such practices and imaginaries can also be observed in other spaces of the ruptured city. Specifically it explores the relocation decisions made by citizens who were subjected to a globally unprecedented state-buyback scheme. Through a typological analysis I argue that, rather than representing submission to state recovery ideologies, the decisions made by movers invoked new kinds of identity, meaning and value amongst participants. In doing so this paper explores the multiple spaces through which ethical fidelity to the perceived transformational nature and potential of the disaster has been performed. Such analysis problematises narratives of what constitutes 'recovery' and raises questions about how the transformational potential of disasters pervade landscapes through multiple spaces and scales. [ABSTRACT FROM AUTHOR]

The vulnerabilities that turn natural hazards such as hurricanes and earthquakes into disasters are rooted in inequality. The overarching aim of this article is to shed light on the nexus between social justice principles and the mitigation of disaster impacts. The article uses 'disaster justice' – an offshoot of 'environmental justice' – as a conceptual lens to help enrich understanding of the differential impacts of disasters upon communities and to craft more inclusive policies and programmes. In India, among the world's most disaster-prone countries, as elsewhere, there is growing recognition that children and youth, who assume a disproportionate share of the burden created by disasters, are an underutilized demographic force multipliers for disaster risk reduction. The country's multi-hazard risk and vulnerability profile has led Indian non-governmental organisations (NGOs) not just to provide post-disaster relief but increasingly to integrate into their work programs designed to unleash the potential of young people to develop the resilience of their communities. [ABSTRACT FROM AUTHOR]

Emergency disaster managers are concerned with responding to disasters in a timely and efficient manner. We are concerned with determining the location and amount of disaster relief supplies to be prepositioned in anticipation of disasters. These supplies are stocked when the locations of affected areas and the amount of relief items needed are uncertain. Furthermore, a proportion of the prepositioned supplies might be damaged by the disasters. We propose a two-stage robust optimization model. The location and amount of prepositioned relief supplies are decided in the first stage before any disaster occurs. In the second stage, a limited amount of relief supplies can be procured post-disaster and prepositioned supplies are distributed to affected areas. The objective is to minimize the total cost of prepositioning and distributing disaster relief supplies. We solve the proposed robust optimization model using a column-and-constraint generation algorithm. Two optimization criteria are considered: absolute cost and maximum regret. A case study of the hurricane season in the Southeast US is used to gain insights on the effects of optimization criteria and critical model parameters to relief supply prepositioning strategy. [ABSTRACT FROM AUTHOR]

Using our professional experiences with natural disaster relief, as well as existing theory, the authors introduce an equity-oriented framework – Social Justice Disaster Relief, Counseling, and Advocacy. We then present the case of the 2017 Northern California wildfires using responses from 259 individuals who were living in the region of the Northern California wildfires – the most destructive fires in California state history. We collected qualitative and demographic data on each participant 3 months after the fires ended. Qualitative data included detailed written reflections to a prompt from each participant in response to an online questionnaire. A thematic analysis using open, axial, and selective coding was conducted to gain a deeper understanding of the context, circumstances, and outcomes of recurring themes, concepts, categories, and their relationship to core themes. Results reveal six themes including: loss and displacement; physical and psychological impact; exploitation; social inequities against vulnerable communities; community strengths and support; and the need for support and services. The themes highlight essential directions for Counseling Psychologists working with survivors of a natural disaster. We offer recommendations for training, theory, counseling, and research, as a means for supporting and advocating for clients psychologically, socially, and emotionally in the context of a natural disaster. [ABSTRACT FROM AUTHOR]

In classic Markov Decision Processes (MDPs), action costs and transition probabilities are assumed to be known, although an accurate estimation of these parameters is often not possible in practice. This study addresses MDPs under cost and transition probability uncertainty and aims to provide a mathematical framework to obtain policies minimizing the risk of high long-term losses due to not knowing the true system parameters. To this end, we utilize the risk measure value-at-risk associated with the expected performance of an MDP model with respect to parameter uncertainty. We provide mixed-integer linear and nonlinear programming formulations and heuristic algorithms for such risk-averse models of MDPs under a finite distribution of the uncertain parameters. Our proposed models and solution methods are illustrated on an inventory management problem for humanitarian relief operations during a slow-onset disaster. The results demonstrate the potential of our risk-averse modeling approach for reducing the risk of highly undesirable outcomes in uncertain/risky environments. [ABSTRACT FROM AUTHOR]

The post-disaster need assessment for the affected area is of significance for the effective distribution of relief supplies. This paper innovatively concentrates on the integrated routing optimization problem for the rapid need assessment stage and the detailed need assessment stage. A bi-objective model with consideration of the effects of site familiarity and route familiarity is proposed. The proposed nonlinear model is equivalently transformed into a mixed-integer linear programming. A tabu search algorithm is designed to solve the proposed model, and its solving accuracy is validated by comparing with the solution of CPLEX based on a small-scale problem. To demonstrate the efficiency of the proposed model and algorithm for large-scale problems, the case of Ya'an earthquake in China is investigated. The experimental results reveal that comparing to the separate routing optimization and the sequential routing optimization, the integrated routing optimization can achieve a better coordination between the decisions of two stages. [ABSTRACT FROM AUTHOR]

The PETS Act amended the Robert T. Stafford Disaster Relief and Emergency Assistance Act to ensure state and local emergency preparedness operational plans included provisions for individuals with household pets and service animals following a major disaster or emergency. While the national newsworthy events are usually large scope disasters, there are also local events which do not meet the minimal conditions to qualify for state or federal level welfare support. Local governments should be prepared to house pets at evacuation shelters, regardless of the breadth of the event, and especially for local events that do not reach the level of state or federal disasters. Wake County, North Carolina developed a concept of prepared sustainment "PET PODs" equipped with inventory and operational instructions required to create and operate an animal evacuation shelter, which could be, or not be, closely associated with the human evacuation shelter. The Wake County Animal Center "PET PODs" provided supplies for animals which arrived with their owners at evacuation shelters during Hurricane Florence, which in addition to volunteer personnel and donated feed provisions, established the foundation for good animal welfare for dozens of animals. [ABSTRACT FROM AUTHOR]

Changing environmental conditions are creating conditions that are leading to an increasing number of disasters. University libraries are at risk of impact from these disasters. The nature of library collections and services is highly intertwined with the physical spaces within the institutions. Thus, the consequences of these disasters have a significant effect on the knowledge ecosystem. Epistemological theories have rarely been explored and yet are important theoretical foundations to frame disaster recovery activities. Traditionally disaster management and recovery have lacked theoretical frameworks and been operationally focused. Approaches have been based on a rationalist framing of the problem and a presumption that there is a justified truth belief that the previous state is the ideal state. In this article, the nature of knowledge through three epistemological approaches is explored and tested on two case studies. Victoria University of Wellington (New Zealand) and the Australian National University (Australia) are used to assess the operational and epistemological issues of disaster recovery. The paper reflects on challenges to the role of the library and the application of epistemological theories to learn lessons for future approaches to disaster recovery. It poses questions about how libraries can take the opportunity to rethink in the digital environment. [ABSTRACT FROM AUTHOR]

We previously developed a four-arm, four-flipper disaster response robot called OCTOPUS to perform complex tasks at severe disaster sites. Its 22 degrees of freedom (DOF) enable advanced mobility and workability, but requires a minimum of two operators to control it. Considering the limitations of the current automated systems and the need to reduce human resources, it is practical to control OCTOPUS through a shared control. This entails a human-automation (HA) control system that independently allocates control authority between a human and automated system so as to simultaneously cover all DOF in what we call 'separative shared control'. To establish an HA control system, control authority allocation (CAA) must be carefully designed. We first performed experiments with a human-human (HH) control system using a VR simulator. We determined four tasks and five CAAs and then selected the optimum CAA by analyzing the results. We then developed an HA control system utilizing the selected CAA that replaces either human operator with a simple automated system. We compared the performance of the HH and HA control systems in the above four tasks using the VR simulator and found that the HA achieved less workload and higher subjective usability than the HH. [ABSTRACT FROM AUTHOR]

Information and Communication Technology (ICT) infrastructure failure due to disasters result in huge monetary losses and jeopardize entire communication networks, directly affecting Public Protection and Disaster Relief (PPDR) operations. The absence of dedicated harmonized frequency bands for public safety and disaster relief communication is a major concern in India. It limits system functionalities and rescue efforts of Indian Public Protect and Disaster Relief (PPDR) agencies. In this paper, we conduct a survey of proposed and existing disaster management systems and emergency communication spectrum being used in developed countries, specifically Europe and the US and look at possible candidate frequency bands suitable for PPDR communication in the Indian scenario. We also look at global standards and regulatory frameworks drawn up by international standardization organizations for PPDR communication and spectrum allocation. We expand our scope of discussion by looking at ICT infrastructure currently being used in India to tackle large-scale PPDR operations. We further explore the possible system and design upgrades that may be carried out in future to support advanced early warning systems and state-of-the-art broadband PPDR networks. [ABSTRACT FROM AUTHOR]

The establishment and use of community-based natural resource management (CBNRM) groups is now the established route towards conservation in large parts of the Pacific, especially in Fiji. One of the main strengths of CBNRM that is often mentioned is its adaptability to different contexts and to changing objectives among stakeholders, an aspect thoroughly tested by events such as 'natural' disasters. This paper is based on 10 months of fieldwork in Kubulau district in Fiji before, during and after Cyclone Winston. It provides an ethnographic account of how this 'natural' disaster affected a community and the local CBNRM group. It looks at how this event was experienced and managed by the group, and how the group's involvement in disaster assistance shifted its role both within and outside of the community, as well as changing its future agenda during the months that followed. We can see in the aftermath of disaster a shift within the group away from the original primary focus on conservation and towards development. The paper provides an account of how the disaster seemingly strengthened the group and the 'community' in terms of organisation, knowledge and motivation, but also how it challenged previous norms and exposed weaknesses that are arguably inherent within CBNRM itself. [ABSTRACT FROM AUTHOR]

In this paper, I examine the disconnect between government and NGO responses to Cyclone Pam, and previous healthy food initiatives in Port Vila, Vanuatu. Decades of nutrition education programs across Vanuatu have urged families to limit their consumption of tinned meat and imported food in favour of locally grown fruits and vegetables. These dietary guidelines call on families to watch their food consumption in order to raise healthy children and avoid serious non-communicable diseases such as hypertension and diabetes. In spite of these guidelines, the food aid offered after Cyclone Pam, on which many ni-Vanuatu depended, consisted primarily of imported white rice, tinned meat, and instant noodles – foods previously labelled unhealthy in nutrition education campaigns. Humanitarian agencies often envision disaster relief as a short-term intervention. However, the cyclone exacerbated inequality across Port Vila, magnifying socioeconomic disparities and associated food insecurity that existed before the cyclone, forcing many families to rely reluctantly on unhealthy food aid. Drawing on research conducted over several fieldwork trips from 2010 to 2016, I discuss food security and nutrition programs in Vanuatu and how ni-Vanuatu families interpreted disaster relief efforts in the context of existing nutrition programming. I argue that disaster preparedness has not accounted for the values promoted in nutrition education programs, the health needs of the community, or food preferences. I conclude with a call for the creation of culturally appropriate responses to natural disaster relief efforts so as to promote sustainable food security measures. [ABSTRACT FROM AUTHOR]