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Disasters affect millions of people annually, causing large numbers of fatalities, detrimental economic impact and the displacement of communities. Policy-makers, researchers and industry professionals are regularly faced with these consequences and therefore require tools to assess the potential impacts and provide sustainable solutions, often with only very limited information. This paper focuses on the themes of "disaster management", "natural hazards" and "simulation", aiming to identify current research trends using bibliometric analysis. This analysis technique combines quantitative and statistical methods to identify these trends, assess quality and measure development. The study has concluded that natural hazards (73%) are more predominant in research than man-made hazards (14%). Of the man-made hazards covered, terrorism is the most prevalent (83%). The most frequent disaster types are climate related, and in this study hydrological (20%), geophysical (20%), meteorological (15%) and climatological (5%) were the most frequently researched. Asia experiences the highest number of disaster events as a continent but in this study was only included in 11% of papers, with North America being the most recurrent (59%). There were some surprising omissions, such as Africa, which did not feature in a single paper. Despite the inclusion of key words "simulation" and "agent based" in the searches, the study did not demonstrate there is a large volume of research being carried out using numerical modelling techniques. Finally, research is appearing to take a reactive rather than proactive approach to disaster management planning, but the merit of this approach is questionable. [ABSTRACT FROM AUTHOR]

Energy is known as the lifeline of national development, and from the last decade or so, the security of human energy supply has been frequently disturbed by natural disasters due to global warming and frequent geological activities. In this paper, by combing the literature, we found that the past literature on the relationship between energy and natural disasters mainly focused on measuring energy consumption under natural disasters, but less on the integrated evaluation of human economic activities, energy security and natural disasters, and less on the evaluation of the energy governance efficiency of each provincial, municipal, and autonomous region government in preventing and managing exposure to natural disasters from the perspective of Chinese provinces. Therefore, in order to fill the gap in the literature in this field, this paper collects panel data of energy consumption efficiency stages and natural disaster treatment stages from 2013 to 2017 for 30 provinces in China (excluding Hong Kong, Macao and Taiwan as well as Tibet Autonomous Region) and uses the two-stage undesirable dynamic DDF model as a framework to study the relationship between economic, environmental pollution and natural disasters for the sample data and to analyze the phase-by-phase evaluation of energy and natural disaster efficiency and make corresponding policy recommendations. The following conclusions are drawn: (1) The overall efficiency of China's eastern coastal provinces is higher than that of the central and western provinces. (2) The first-stage efficiency of Chinese provinces is better than the second stage, and the difference in efficiency of the first stage of each province is smaller than that of the second stage. (3) In terms of the efficiency of disaster prevention and relief inputs, the efficiency values are generally lower in most regions of China. (4) Finally, the annual efficiency of natural disaster losses is not high in all regions, and the efficiency values are higher in the eastern coastal regions than in the central and western regions. Accordingly, this paper proposes that each province should formulate relevant disaster prevention and economic development strategies according to regional characteristics, while the central government should also propose locally appropriate coordinated governance policies to effectively control carbon dioxide emissions and air pollution, as well as increase disaster prevention publicity and incorporate disaster prevention education-related work into the performance assessment mechanism of local governments to promote the interactive development of the two. [ABSTRACT FROM AUTHOR]

The B Fifth World Landslide Forum b is organized under the subtitle Implementing and Monitoring the Sendai Landslide Partnerships 2015-2025, I Voluntary contribution to the Sendai Framework 2015-2030 i , and the I Agenda 2030-Sustainable Development Goals. i All session proceedings to the WLF5 secretariat will be compiled as WLF5 electronic proceedings with ISBN number. Each poster can be cited by its number within the WLF5 electronic posters published by the WLF5 organizing committee. Potential WLF5 supporting organizations willing to support more than 3000 USD or 300,000 JPY are requested to inform it to the financial support evaluation team (FSET) of WLF5 secretariat;. [Extracted from the article]

Social media has become an essential channel for posting disaster-related information, which provides governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention, and extracting useful information is still challenging. This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes toward disaster response and public demands for targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41,993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response, and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models. To better provide decision-makers with the appropriate model, the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and the evolution of peoples' behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster management. [ABSTRACT FROM AUTHOR]

The field of supply chain management has been extensively studied, while its role in disaster relief operations has received limited contributions. Recent dedicated special issues on Humanitarian Operations and Supply Chain Management (HOSCM) and a dedicated Journal focusing on humanitarian logistics and supply chain management clearly indicate the growing popularity of the HOSCM literature. The purpose of our current study is to undertake an extensive review of extant literature published in operations and supply chain management journals as well as popular interdisciplinary journals. A review of 362 papers published between 2011 and 2017 provides a thematic outline of the study. The study pivots around nine key themes, which have gained prominent attention from HOSCM scholars, and draws a roadmap from reviewing earlier review papers to performance evaluation of HOSCM related studies. Some of the key themes include humanitarian logistics, theory focused research, case studies, mathematical models, humanitarian supply chain properties and resources needed for efficient and effective management of humanitarian operations. Finally, our study offers several research directions which may take the existing debates to a next level. [ABSTRACT FROM AUTHOR]

Large-scale disasters occur worldwide, with a continuing surge in the frequency and severity of disruptive events. Researchers have developed several optimization models to address the critical challenges of disaster relief supply chains (e.g., emergency material reserving and scheduling inefficiencies). However, most developed algorithms are proven to have low fault tolerance, which makes it difficult for disaster relief supply chain managers to obtain optimal solutions and meet the emergency distribution requirements within a limited time frame. Considering the uncertainty and ambiguity of disaster relief information and using Interval Type-2 Fuzzy Set (IT2TFS), this paper presents a collaborative optimization model based on an integrative emergency material supplier evaluation framework. The optimal emergency material suppliers are first selected using a multi-attribute group decision-making ranking method. Multi-objective fuzzy optimization is then run in three emergency phases: early -, mid-, and late-disaster relief stages. Focusing on a massive flash flood disaster event in Yunnan Province as a case study, a comprehensive numerical analysis tests and validates the developed model. The results revealed that the proposed optimization method can optimize emergency material planning while ensuring that reserve material safety inventory is always maintained at a reasonable level. The presented method suggests a fuzzy interval to prevent emergency materials' safety inventory shortage and minimize continuous life/property losses in disaster-affected areas. [ABSTRACT FROM AUTHOR]

Nowadays, logistics is one of the most important tools in disaster relief operations. The logistic planning is essential and a key component in covering the initial needs in the immediate aftermath of any disaster. Planning is both necessary and practical, as it is generally possible to predict the types of disasters that should affect a given location and the needs that such disasters will be likely to cause. Transport planning, reception and distribution of emergency supplies, type and quantity of the resources, the way of procurement and storage of the supplies, the tools of the tracking and means transportation to the stricken area, the specialization of teams participating in the operation and plan of cooperation between these teams, are some vital life-saving coordination roles after natural disasters are connected directly to logistic planning. Turkey is located in one of the most active earthquake and volcanic regions which causes to many major earthquake-prone, in the world with a majority of the population living in these earthquake-prone areas. Earthquakes are one of the major disasters that require emergency logistic planning strategies due to their devastating effects, the large-scale natural disasters could cause major problem on commodities such as food, medicine etc. In this paper, a game theoretical model for emergency logistic planning is developed. To do this a cooperative game model is constructed from a flow problem which occurred after an earthquake in Istanbul. Several solution concepts for maximizing the transferred commodity are given. The paper ends with a conclusion and outlook to future studies. [ABSTRACT FROM AUTHOR]

Research for efficiently planning and responding to natural disasters is of vital interest due to the devastating effects and losses caused by their occurrence, including economic deficiency, casualties, and infrastructure damage. Following the large breadth of natural disasters such as Hurricane Katrina in 2005, and the earthquake in Haiti in 2010, we observe a growing use of game theoretic models in the research concerning natural disaster management. In these models, government agencies and private companies interact as players in a disaster relief game. Notable research in these areas has studied multi-player games and multi-agency collaboration, among others, to provide insights into optimal decisions concerning defensive investment and private-public partnerships in the face of disaster occurrence. This paper aims to increase the comprehension of game theory-based research in disaster management and to provide directions for future research. We analyze and integrate 57 recent papers (2006-2016) to summarize game theory-based research in natural disaster and emergency management. We find that the response phase of disaster relief has been researched most extensively, and future research could be directed toward the other phases of disaster management such as mitigation, preparedness, and recovery. Attacker-defender games to be utilized relatively frequently to model both mitigation and response for a disaster. Defensive resource allocation and sequential/simultaneous games to model the interaction between agencies/individuals in light of a disaster are two other common ways to model disaster management. In addition to academia, the targeted audience of this research includes governments, private sectors, private citizens, and others who are concerned with or involved in disaster management. [ABSTRACT FROM AUTHOR]

Online Social Media, such as Twitter, Facebook and WhatsApp, are important sources of real-time information related to emergency events, including both natural calamities, man-made disasters, epidemics, and so on. There has been lot of recent work on designing information systems that would be useful for aiding post-disaster relief operations, as well as for pre-disaster preparedness. A special issue on “Exploitation of Social Media for Emergency Relief and Preparedness” was conducted for the journal Information Systems Frontiers. The objective of this special issue was to present a platform for dissemination of the empirical results of various technologies for extracting vital and actionable information from social media content in disaster situations. The papers included in this issue are expected to be the stepping stones for future explorations and technical innovations towards technologies meant for utilizing various online and offline information sources for enhancing pre-disaster preparedness and post-disaster relief operations. [ABSTRACT FROM AUTHOR]

Disasters can have substantial impacts on people's livelihoods in developing countries. Further, if the need for livelihood interventions is ignored or delayed, the crisis may trigger unexpected harmful consequences in the affected households in the aftermath. Therefore, restoring livelihoods should remain a priority in the post-disaster recovery process. However, such recoveries in rural contexts and developing countries, like Nepal, are complex as the livelihood restoration process is affected by serious spatial, socio-economic, and political factors. We employed qualitative research methods in four highly affected districts in the 2015 Nepal Earthquake (7.8 Mw) to examine post-disaster livelihoods recovery. Our paper critically assesses the humanitarian response based on the narratives and lived experiences of affected households. The findings show that humanitarian assistance was crucial in addressing several unmet needs of disaster-affected rural households in resource-poor settings in Nepal. However, the interventions were generally fragmented, insufficient, neoliberal led (forcing market dependencies), and largely business-as-usual in their orientation. Previous studies in Nepal paid insufficient attention to the goods provided to affected households in the name of recovery. Therefore, our paper scrutinises selected humanitarian objects, such as power tillers, and unpacks their political economy and effectiveness in local contexts. Further, our findings show that some livelihood policies reinforced the gap between the haves and have-nots, thereby reproducing pre-disaster inequalities in the post-disaster field. [ABSTRACT FROM AUTHOR]

Agro-pastoral ecotone of northern China is the prominent area for agricultural production, but it is also the most typical ecological fragile area with frequent drought disasters. Taking Yulin City at Shaanxi Province in China as the case area, the paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought in China's agro-pastoral ecotone in the period 2000 to 2020. The results show that: 1) the agricultural vulnerability to drought in Yulin City has shifted from high vulnerability in the period 2000–2010 to low vulnerability in the period 2011–2020. 2) There exist obvious spatio-temporal differences of the agricultural vulnerability to drought in Yulin City during the research period. 3) Four sensitive events and 14 resilient events were identified in the research and the crops of Yulin had become more resilient to drought. Finally, the paper put forward with policy implications to make adaptive strategies of agriculture to climate change in China's agro-pastoral ecotone in the future, e.g., carrying out agricultural zoning based on agricultural production conditions, intensifying the construction of disaster prevention and relief system, and integrating with modern agricultural technology to develop new type agriculture. [ABSTRACT FROM AUTHOR]

Following the increasing rate of disasters' impacts on societies, more attention is being paid to recovery projects after these disasters. However, from the perspective of project management, recovery projects show a significant degree of differences from regular construction projects. Although some progress has been made on this topic, further research is needed to smooth pathways for the management of disaster recovery projects. This paper provides a management framework for disaster recovery projects through a qualitative analysis of the research's selected resources. The resources selected from academic online platforms have been coded by NVivo 2020, and corresponding groups have been made for the disaster recovery lifespan and project stages separately. An integrated management framework has been put forward throughout the simultaneous application of the research's suggested lifespan and project stages. The most significant observation of this study is the necessity of applying a continuous lifespan to address projects' long-term goals and provide an experience-based database to manage the next disasters. The significant role of planning in the entire lifespan of recovery projects, recognition of the tasks that need to be handled continuously during their entire lifespan, the necessity of simultaneous consideration of tangible and intangible recovery outcomes are the other findings of this study. The reconstruction stage is identified as part of the more extensive system, which stresses the necessity of considering the recovery project as a whole system. [ABSTRACT FROM AUTHOR]

Research on the poverty risk caused by geological disasters in disaster-prone areas is a useful exploration to coordinate social economic development with disaster prevention and reduction, and is of great significance to the regional sustainable development. Based on statistical data and spatial data, this paper takes Sichuan Province as the typical research area. Remote sensing and geographic information technology are used to study the poverty risk caused by disasters based on the quantitative evaluation of geological disasters risk and regional development level. The spatial differentiation characteristics of poverty risk caused by disasters are explored on the 1 km × 1 km grid scale. The results indicate that (1) the overall risk of geological disasters in Sichuan Province is relatively high, with high and relatively high risk areas accounting for more than 40% and low and relatively low risk areas accounting for less than 30%. The risks in Mountain and Ravine Areas are significantly higher than other areas. (2) The regional development level in Sichuan Province is relatively high, but with significant spatial differences. The development level of high-altitude areas and remote mountainous areas is quite different from that of the Chengdu Plain in the middle Sichuan Province. The uneven development in the east, middle, and west is a prominent problem. (3) The poverty risk caused by disasters is high, and the spatial pattern presents a characteristic of "high in the west and low in the east" with high positive spatial correlation. High-High Cluster Areas are mainly distributed in western and southwestern Sichuan. Low-Low Outlier Areas are mainly distributed in Chengdu Plain and Hilly Areas of Sichuan Basin. High-Low Outlier and Low–High Outlier Areas occupy a relatively small percentage with scattered distribution. This paper provides some theoretical support for policy formulation and management of coordinated development of regional socioeconomic and ecological environment. [ABSTRACT FROM AUTHOR]

The prediction of disaster risk paths can foresee the spread of disaster risk and lay a foundation for reducing or avoiding the harm of disaster risk. In this paper, we have improved the embedding layer of convolution neural network (CNN), and we literally refer to the specific CNN framework as IECNN. The IECNN model is proposed to predict the disaster risk path. Here, we first establish a disaster risk network in which each node represents two attributes: place and disaster type. The random walk paths are generated from the disaster risk network, and the structural characteristics of nodes in the disaster risk paths are considered in the improved embedding layer of IECNN. This study also applies Markov chain algorithm to calculate the probability of each disaster risk path and trains the IECNN model with the determined disaster risk paths. ROC curve, AUC, Accuracy, F1-measure, Precision, and Recall are used to evaluate the prediction model. In order to verify the feasibility and advantage of the proposed model, we use a dataset consisting of natural disasters in southwest China in the experimental section. Results of the comparative analysis display that the proposed model cannot only effectively predict the disaster risk path, but also provide the high performance in terms of every evaluation index. Moreover, some characters of disaster risk path are found and discussed. Resultantly, our results can provide an efficient way to prevent and control risk spread in disaster events. [ABSTRACT FROM AUTHOR]

Management of disaster relief operations is a complex task involving pre-disaster, during disaster and post-disaster operations. It requires the involvement and coordination of multiple actors and processes. Traditionally, operations research applications in general and specifically for disaster relief have been made, largely, on quantitative and analytical front. There is a lack of qualitative and interpretive approaches in operations research applications. This paper uses an interpretive method, SAP-LAP (situation, actor, process, learning, action, performance) framework and linkages, in the context of disaster management. It enables to develop a theoretical framework for disaster management answering the fundamental questions of theory building. The paper first provides a selective review of disaster management and identifies the gap in building theoretical framework for the same. It then describes the methodology in terms of SAP-LAP framework and linkages with a broad appreciation of its application in operations as well as strategic management. This methodology is applied to develop a theoretical framework for disaster management. The paper finally discusses both the theoretical and practical implications of the proposed framework and concludes with future directions of research. [ABSTRACT FROM AUTHOR]

Twitter is an intensely utilized platform for disaster events and emergencies. Therefore, Twitter is an important resource for providing the essential information. Named entity recognition (NER), which is the process of determining the elementary units in a text and classifying them with pre-defined categories, plays a significant role to extract essential and usefulness information. However, NER is a challenging task due to the utilized informal text in the Twitter platform such as grammatical errors and nonstandard abbreviations. In this paper, recurrent neural network (RNN)-based approaches considering diversity of activation functions and optimization functions with NER tools are utilized to extract named entities such as organization, person, and location from the tweets. Inputs for RNN models are provided via two different NER tools which are natural language toolkit (NLTK) and general architecture for text engineering (Gate). Then, pre-labeled data are trained via GloVe word embedding technique, and RNN model variants such as LSTM, BLSTM, and GRU are demonstrated. Therefore, outperforming models among RNN variants are presented for predicting named entities. Yellowbrick interpreter is used for evaluation of the proposed method and Wilcoxon signed-rank test are applied on results of two different data sets to demonstrate consistency of the proposed method. In addition, comparison is made with existing machine learning methods. The experiments by utilizing the Nepal earthquake Twitter data set show that the RNN-based approaches achieve good results in finding named entities. In emergencies, the results of this paper can help in reducing the efforts of event location detection and provide better disaster management. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic disrupted life as usual around the globe. Efforts to control the spread of the virus with lockdowns and border closures pushed millions of people into food and social insecurity. Most research on humanitarian organizations have been dominated by the uncertainty and urgency of disaster response operations. However, some humanitarian organizations also operate in long-term continuous aid programs where efficiency is the key goal. We analyzed the operations of food banks in the Feeding America network and The Salvation Army USA, and found them to be ambidextrous organizations. The ambidextrous humanitarian organizations like food banks and Salvation Army, focus on long-term continuous aid programs, specifically pertaining to the sustenance of the communities they serve, but also play a key part as first responders or as local agencies aiding in disaster relief and response. We propose a framework to analyze disaster, development, and sustenance aid supply chains, and identify future research opportunities. [ABSTRACT FROM AUTHOR]

The post-disaster humanitarian logistic operations deal with the supply of emergency relief materials to mitigate damages in the affected areas. Immediately after the disaster, it is challenging to estimate the demand for emergency relief materials. As a result, the demand for such materials at the point of demand and the corresponding transportation costs for the entire supply chain network becomes uncertain. This paper proposes a new probabilistic fuzzy goal programming model for making decisions to manage the post-disaster supply of emergency relief materials. A suggested procedure converts the proposed model to its deterministic equivalent when the demands for the relief materials follow uniform distributions. We implement the differential evolution, a metaheuristic technique, for analyzing demand satisfaction for relief materials under various scenarios. A case example based on the Nepal Earthquake in 2015 demonstrates the usefulness of the proposed approach. The solution of the model will help the Disaster Management Agency coordinate with the humanitarian organizations and foreign countries to provide the required emergency relief materials so that an adequate level of supply can be assured to the affected areas with the least possible transportation cost. [ABSTRACT FROM AUTHOR]

Quality management has been widely discussed in the literature, and recent special issues on humanitarian supply chains and relief operations have emphasized the increasing importance of quality management in this key emerging area. In this paper, we provide an extensive literature review in the field of quality management in humanitarian operations and disaster relief management. Our comprehensive review, comprising 61 articles published from 2009 to 2018, leads to the identification of enablers (e.g., transparency, policy framework), challenges (e.g., financial services, identity protection), and theory development approaches, as well as numerous research gaps that must be addressed. [ABSTRACT FROM AUTHOR]

Due to China's exposure and susceptibility to natural hazards, it is important for governments and aid agencies to have tools that enable effective post-disaster livelihood recovery in creating self-sufficiency for the affected population. This paper introduces a framework of critical components that constitute a successful livelihood recovery by studying the perceptions and perspectives of those affected by the 2013 Lushan earthquake in Sichuan Province. A mixed method, namely a pilot study, questionnaire survey and interviews, was used. While the populations surveyed valued the employment benefits of creating new market and economic sectors in areas where rural farming was predominant prior to the earthquake, they considered that recovery of their housing was most essential for livelihood diversification. External assistance was also regarded highly in assisting with livelihood recovery, in which family ties and social connections seemed to have played a larger role than that of government agencies and NGOs. Lastly, the results highlighted that a goal of livelihood recovery cannot be achieved without individual well-being being considered, in relation to people's perceptions about quality of life and their physical and mental health. This paper aims to assist the Chinese policy makers and practitioners in disaster recovery in setting an agenda of preparing for livelihood recovery in peace times, or of post-disaster recovery planning should a disaster event occurs so that the economic impact and livelihood burdens of those affected can be effectively mitigated. [ABSTRACT FROM AUTHOR]

The Fifth World Landslide Forum (WLF5) was postponed from 2-6 November 2020 to 2-6 November 2021 due to COVID-19. Full color books Six volumes of full color books titled I Understanding and Reducing Landslide Disaster Risk i which are the inaugural volumes of the new ICL book series "ICL contribution to landslide disaster risk reduction" (ISSN 2662-1894 (print version) and e-ISSN 2662-1908 (electronic version)) will be published in January 2021. The six volumes of books include all accepted papers submitted to WLF5. [Extracted from the article]

Developing an effective safety emergency plan for coal mines is crucial to prevent and reduce accidents, as well as improve the emergency response capability. This paper identifies and analyzes potential accident hazards in the Burtai coal mine through a comprehensive and practical investigation of risk factors. Based on a mathematical model of a mine fire disaster relief algorithm, the MATLAB software is used to conduct numerical simulations of the dynamic spreading process of the fire smoke. Several escape routes are determined based on the simulation results, including the main escape route at the working face of coal mining, the main escape route at the working face of tunneling, and the main escape route against the reverse wind in the main inclined shaft, the auxiliary adit, and the main air intake roadway. The results presented in this study can provide guidance for improving fire emergency escape and rescue plans of the Burtai coal mine. [ABSTRACT FROM AUTHOR]

As a new material, polyurethane polymer has been widely used in emergency and disaster relief engineering in recent years due to the excellent engineering mechanical properties. Based on the characteristics of this material, a multi pipe grouting micro anti-slide pile is proposed in slope reinforcement, which is formed by using polyurethane polymer slurry as grouting material. Compared with traditional anti-slide pile, the polyurethane polymer micro anti-slide pile has the advantages of strong applicability, no water reaction, small disturbance, fast construction, economy and durability, and it can be adapted to emergency reinforcement of dangerous landslide. As a flexible retaining structure, polyurethane polymer micro anti-slide piles can strengthen the slope by cooperating with the forces. However, there is no report on the reinforcement of slope by polyurethane polymer micro anti-slide piles at present. In this paper, a three-dimensional multi-row polyurethane polymer micro anti-slide piles model for slope reinforcement considering different embedded depth and pile location is established. Safety factor, thrust force of landslide behind pile, length of pile and Mises stress are taken as four factors to evaluate the feasibility and the reinforcement effect of reinforcing slope with polyurethane polymer micro anti-slide pile. The optimal reliability of polyurethane polymer micro anti-slide pile for slope reinforcement is evaluated by giving different weight values to each factor through multi-factor comprehensive evaluation method. The safety factor of slope (Fs), landslide thrust behind pile and Mises stress of pile are analyzed under different embedded depth (le) and pile position (px). The results show that polyurethane polymer micro anti-slide piles have excellent reinforcement effect under rescue and relief tasks. With the increase of embedded depth, the safety factor of slope gradually increases and then remains stable, the best embedded depth of micro-pile is about 1/8–1/12 LB; as the pile position is gradually away from the top of the slope, the safety factor of the slope reaches its maximum value in the middle and lower part of the slope, the optimum position of pile arrangement is 0.55–0.65 L from the top of the slope. [ABSTRACT FROM AUTHOR]

Crowdsourcing and Internet of Things (IoT) are gaining more and more attention both in industry and academia in order to explore their effects on disaster relief. The current state of the literature shows a clear focus on the extent to which crowdsourcing on one hand, or IoT on the other hand, can individually make a difference regarding disaster response, but very few studies have considered the integration of both crowdsourcing and internet of things in order to link them with disaster response. Accordingly, in this paper, the authors have attempted to develop a crowdsourcing and IoT integration model which could help improving disaster response by using important value derived from using both social media and RFID technology. Furthermore, despite the fact that disaster relief offers similarities with epidemic transmission, (especially the SIR model), the application of SIR model in disaster relief still remains unexplored, which has led the authors to conduct a series of SIR model-based simulations to investigate the extent to which such integration model helps improving disaster response. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate conceptual and theoretical similarities between disaster relief chains (DRCs) and commercial supply chains using the customer relationship management process model (CRM) of the seminal global supply chain forum framework (GSCF) as a lens of analysis (Croxton et al. in Int J Logist Manag 12(2):13–36, 2001). A range of empirical data from a case study of the 2006 relief and recovery response to the Cyclone Larry disaster is analysed using the CRM process model of the GSCF framework as a lens of analysis. We find that there are unexpected conceptual and theoretical similarities between DRCs in the Cyclone Larry disaster response and commercial supply chains. The study demonstrates that core commercial SCM concepts such as integration and integrative process management can also be found empirically in the domain of disaster response operations and allied management of supply chains for disaster relief and recovery (DROSCM). The study also shows that research in the DROSCM domain can develop in a range of directions unfettered by a paradigm focused on differences between DRCs and commercial supply chains. This is the first study to conceptually, theoretically, and empirically demonstrate similarities between DRCs and commercial supply chains. [ABSTRACT FROM AUTHOR]

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). [ABSTRACT FROM AUTHOR]

Liquefaction is one kind of earthquake-induced disasters which may cause severe damages to roads, highways and buildings and consequently delay the disaster rescue and relief actions. A fast and reliable assessment of liquefaction disaster is thus of great importance for making disaster prevention plans beforehand and for planing rescue and relief activities right after earthquakes. However, this is still a great challenge task, because the computational cost of current existing liquefaction assessment methods is very high. For example, a 50 seconds simulation (5000 time steps) needs one hour with 1000 nodes in the Supercomputer K. In this paper, we proposed a machine learning based liquefaction disaster assessment method. Here, the assessment result can be given with high efficiency (few seconds or less) for emergency evacuation in an earthquake. Meanwhile, a multi-layer approach was also proposed. Firstly, the most dangerous area will be shown immediately by using convolutional neural network (CNN) model; followed by a high precision result, which is obtained by using fast Fourier transform and a special of soil (N values) coupled with a Light Gradient Boosting Machine (Light GBM) model. One more contribution is our visualization design, which can be used to let users know the dangerous area more intuitively. Finally, the effectiveness of our proposed method was demonstrated by assessing liquefaction from a large-scale earthquake simulation. [ABSTRACT FROM AUTHOR]

In the aftermath of natural disasters, emergency medical teams (EMTs) are dispatched to help local rescue efforts. While some impact evaluations of EMTs are available, few comprehensive evaluations of the implementation of EMTs in natural disasters, have been published to date. As a result, the evidence base to inform global guidelines and best practices, is remarkably thin. This paper aims to provide a better understanding of the role of EMTs, by summarizing recent reports and case studies. Specifically, this summary aims to identify key improvement areas, as well as obstacles and opportunities for improvement. After a search of the literature, 40 publications met the inclusion criteria, and were included in this summary of the literature. The effective functioning of EMTs is codependent on interactions between different actors, including national governments, international organizations, NGOs, local government agencies, community stakeholders and the private sector. Five key improvement areas were identified: (1) coordination and integration of EMTs and other actors; (2) systematic classification and registration of EMTs; (3) national stewardship; (4) community engagement; (5) research and data collection. As the prevalence of natural disasters rise, effective disaster response will be an increasingly important component of global health in the coming decades. To optimize EMT efficiency, there needs to be increased recognition of the different actors involved, increased cooperation amongst EMTs under the coordination of international rosters, and increased research efforts to evaluate challenges to and opportunities for improved disaster response. [ABSTRACT FROM AUTHOR]

Information about how agencies behaved in previous disasters could allow us to predict agency behavior and network structure in response to future events. In this paper, we review studies of two different disasters that occurred in the USA to provide insight into some of the common characteristics of disaster relief efforts. Specifically, the studies discussed explore how agencies involved in disaster relief operations formed and maintained partnerships by comparing survey results from Joplin, Missouri (EF-5 Tornado in 2011) and the New Jersey Coast (Hurricane Sandy in 2012). The objective of this paper is to analyze partnership creation, length, and conclusion in networks of agencies responding to disasters. 80 agencies were interviewed and over 500 partnerships were analyzed. The analysis of the data provides a building block to guide the development of inputs for future models of agency behavior and interaction. One key result was that the survey data indicated only a third of the strategic and tactical partnerships that were leveraged to support the relief effort were formalized prior to the disaster event. Additionally, it was found that partnerships between Non-Governmental Organizations were the most stable relationship. This paper provides insight into how agencies involved in disaster relief could manage their partnerships to achieve their goals. [ABSTRACT FROM AUTHOR]

Disaster response involves the planning, coordination, and distribution of supplies in an effective manner to people in need. Recent natural hazards, such as hurricane Harvey, have exposed the complexities and challenges associated with those tasks. In this paper, a stochastic programming model is presented which considers prepositioning strategies among food bank facilities located in high-risks areas for hurricanes. The model considers the uncertainty associated with the impact of the hurricane at each facility in terms of the number of available supplies, donations received at the facility, and the expected demand for their service region. The first-stage decision attempts to minimize the number of people not receiving the needed supplies by prepositioning the existing supplies at each facility. Second-stage decisions maximize the system responsiveness by trying to satisfy the observed demand for the scenarios under consideration. The experiments consider scenarios in which one or two food bank facilities are shut down after the natural phenomenon and study the impact of prepositioning supplies. [ABSTRACT FROM AUTHOR]

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Long-term outcomes and collaborative governance are particularly worthy of discussion when examining the recovery process from natural disasters. In the long and dynamic processes of recovery, how do the primary participants responsible for recovery adapt and change to meet social and economic objectives? This paper argues that the approach to recovery following the Wenchuan Earthquake in China experienced a transformation from paired assistance under centralized political mobilization to collaborative networks. Based on an in-depth case study of the Shanghai–Dujiangyan Paired Assistance Policy, we find that recovery from the Wenchuan Earthquake can be demarcated and described in terms of three stages involving government-oriented project aid, contract-oriented industrial redevelopment, and network-oriented collaborative governance. Four determinants contributed to these transformation processes: (1) The central government was promoted as the responsible entity for guiding recovery efforts from the catastrophe, and the effective political mobilization under the Paired Assistance Policy (PAP) and interactions between subnational assisting governments ensured the timeliness, diversity, and quality of aid projects; (2) industry recovery policies provided the policy environment for enterprises to participate in industrial recovery, and beneficial reciprocity became the key attraction for outside private enterprises to participate in the industrial recovery of Dujiangyan; (3) the participation of social organizations as emergent organizations in the recovery contributed to the formation of collaborative networks; and (4) the trust, friendship, and mutually beneficial relationships that were developed over the 3-year term of the PAP provided a strong foundation for the formation of collaborative networks, which were essential to facilitating longer term recovery. [ABSTRACT FROM AUTHOR]

The purpose of this study is to analyze optimization-based decision-making models for the problem of Disaster Recovery Planning of Transportation Networks (DRPTN). In the past three decades, seminal optimization problems have been structured and solved for the critical and sensitive problem of DRPTN. The extent of our knowledge on the practicality of the methods and performance of results is however limited. To evaluate the applicability of those context-sensitive models in real-world situations, there is a need to examine the conceptual and technical structure behind the existing body of work. To this end, this paper performs a systematic search targeting DRPTN publications. Thereafter, we review the identified literature based on the four phases of the optimization-based decision-making modeling process as problem definition, problem formulation, problem-solving, and model validation. Then, through content analysis and descriptive statistics, we investigate the methodology of studies within each of these phases. Eventually, we detect and discuss four research improvement areas as [1] developing conceptual or systematic decision support in the selection of decision attributes and problem structuring, [2] integrating recovery problems with traffic management models, [3] avoiding uncertainty due to the type of solving algorithms, and [4] reducing subjectivity in the validation process of disaster recovery models. Finally, we provide suggestions as well as possible directions for future research. [ABSTRACT FROM AUTHOR]

In September 2017, Hurricane Maria made landfall on Puerto Rico causing 102 billion worth of damages, demolishing the electric grid and severely affecting essential daily services. Disaster recovery continues as of the onset of the second half of 2020. Our research investigates whether the postdisaster population mobility was greater than the long-term out-migration that has followed the economic recession. This paper makes use of longitudinal demographic and economic data spanning Hurricane Maria to give a long-term view of population change in Puerto Rico. First, we examine population and employment trends in relation to hurricanes and significant economic events and find that population change is more responsive to employment shocks than hurricane events. Second, we examine air passenger travel data to estimate net migration between 2010 and 2018. The data reveal a sharp spike in out-migration following the hurricanes as well as elevated in-migration in the first half of 2018. In short, the evidence presented suggests that the struggling economy is the main cause of net out-migration from Puerto Rico, while out-migration in response to the hurricane-related destruction is mostly temporary. Consequently, plans for Puerto Rico's recovery should focus on stimulating the economy. [ABSTRACT FROM AUTHOR]

In recent years, more and more disasters occurred. Additionally, the amount of people affected by disasters increased. Because of this, it is of great importance to perform the relief operations efficiently in order to alleviate the suffering of the disaster victims. Immediately after the occurrence of a disaster, there is an urgent need for delivering relief goods to demand locations and affected regions, respectively. Due to roads being blocked or damaged by debris, some demand locations may be out of reach and therefore the delivery of relief goods is hampered. This paper investigates the basic problem of simultaneously unblocking roads in order to make demand locations accessible and delivering relief goods in order to satisfy demand. Strict deadlines for the delivery of relief goods are considered at the demand locations. A formal problem statement is provided, and its computational complexity is analyzed. Additionally, a mixed integer programming model is developed and an exact solution method based on a branch and bound approach is proposed. A computational study investigating the performance of the model formulation and the branch and bound algorithm is conducted. [ABSTRACT FROM AUTHOR]

As the stages of emergency response shift, it is an important task in the early stage of emergency response to handle emergency supply (ES) collection with fuzzy demand and multi-hub ES collection network. Taking into consideration the constraints such as no limit to collection time, capacity constraint to single hubs and different demand levels to be met by collection, this paper aims at establishing a mathematic planning model with two objectives, namely the shortest collection time and the optimal collection cost. In terms of algorithm design, the sequential enumeration method (SEM) was employed to solve the model, for higher iteration efficiency and lower difficulty level of the algorithm. The first step was to find the optimal ES collection time, collection route and allocation program in the direct shipment mode, the hub-and-spoke mode and the mixed mode. Then, the three modes were compared in terms of the collection route planning, the shortest collection time, the shortest collection route and the optimal collection cost of the entire ES collection network. The numerical example showed that compared with the simulated annealing (SA) algorithm, the result of SEM is basically the same with that of SA algorithm; however, SEM is simpler, more practical and easier to operate, so that it is of better promotional value. [ABSTRACT FROM AUTHOR]

This paper investigates a population model which grows as per the Markovian arrival process and is influenced by binomial catastrophes that occur according to renewal process. That is, when a catastrophe attacks, an individual (element) of the population survives with probability p or dies with probability 1 - p , independent of anything else. Using the supplementary variable technique, the steady-state vector generating function (VGF) of the population size distribution at post-catastrophe epoch is obtained in terms of the infinite product of matrices. Further, the VGF of the population size distribution at arbitrary and pre-catastrophe epochs are also deduced. To make the model valuable for practitioners, a step-wise computing process for evaluation of the distribution of population size at various epochs is given. A recursive formula to compute factorial moments of the population size is also presented. Finally, some numerical results are included to illustrate the impact of parameters on the behavior of the model. [ABSTRACT FROM AUTHOR]

The joint event of 20th International Symposium on Geo-disaster Reduction (20th ISGdR) was held onsite and online during 16–19 August 2022 in Kanazawa City, Ishikawa Prefecture, Japan. This symposium focused on the theme of "the reduction of the disasters triggered by geological and geophysical factors on the earth and beyond", providing a platform for academics, researchers and practitioners from different countries to discuss the research progress and exchange ideas on the reduction of disasters. The symposium consisted of keynote lecture, invited lectures and other presentations in parallel session. A technical trip to Hakusan Tedorigawa Geopark was conducted on August 19. [ABSTRACT FROM AUTHOR]

Disasters are cataclysmic events that cause significant loss of natural, human and financial resources. The repercussions are exaggerated by climate change making disaster management a hot topic for academic research. The exponential growth in the utilization of ICT (Information and Communication Technology) with other domains has motivated the scientific community to integrate it with disaster management. The aim of this paper is to put forward a scientometric examination to assess the corpus of research performed on various types of disasters and use of ICT over the last 10 years. Annual growth of publication output, related subject categories, productivity analysis parameters were calculated for the evaluation of scopus bibliographic data. Moreover, it presents insights into productive journals, co-operation of qualified authors and collaboration of nations from all over the world. It further investigates prominent institutes and co-occurrence of key research topics. This organized study will facilitate the upcoming authors for effective future research. [ABSTRACT FROM AUTHOR]

People are always threatened by natural disasters which usually cause significant losses. Therefore, planning for confronting such situations is a vast dilemma. In this paper, a general model is proposed to address the uncertain demand of disaster-stricken areas. Demand from injured people relates to the vulnerability of regions that depends on the quality of buildings and severity of damage. In the studied problem, the commodities collected from relief centers, donations and storage warehouses are distributed to the shelters. It aims at minimizing the total cost, and unfulfilled demand and maximizing the coverage and accessibility of relief centers. The LP-metric approach is utilized to solve the multi-objective model, and a scenario-based optimization is used to incorporate the uncertainty in the proposed model. Moreover, an LP–GA method is proposed for optimizing large-scale instances. Several problems in different scales are solved to show its flexibility and time-efficiency. Finally, a case study of an earthquake disaster in Amol city in Iran is presented. The obtained results suggest better service in the distressed urban areas. [ABSTRACT FROM AUTHOR]

In this paper, we provide a model which optimizes the allocation of electricity generation systems, in terms of their number and location, in a disaster relief camp. The objectives that this model takes into account are minimization of the total cost of the project and prioritization of those generation systems that perform favourably. Energy and specifically electricity plays an important role in the provision of essential needs like lighting, water purification, heating, ventilation and medical care for displaced people. Disaster relief camps are commonly considered as off-grid projects, so individual generation and control systems are the main means of electrification. To support decision makers in electrification planning for temporary and semi-temporary camps, we propose a bi-objective integer linear programming model. The performance evaluation of technologies such as fuel generators, wind turbines and solar panels is conducted with an MCDM (VIKOR) approach. The model is applied on a hypothetical but realistic map site with data regarding commercially available equipment. The better performance of solar panels regarding the evaluation criteria have made them the dominant applied source of renewable electricity generation system and together with application of micro-grids in the model they have proven to reduce the cost of generation significantly. However, installing fuel generators have been found necessary for facilities which can cause a remarkable damage in case of electricity interruption. The model is promising in helping relief aid agencies to design an electrification project with minimum cost and maximum utility. [ABSTRACT FROM AUTHOR]

The number of people affected by natural disasters or displaced by conflict, persecution, violence or human rights violations has been steadily increasing, doubling in a decade and reaching 141.1 million in 2017 (United Nations Office for the Coordination of Humanitarian Affairs (OCHA) in Global humanitarian overview 2017 (June status report), 2017. http://www.unocha.org/sites/unocha/files/dms/GHO-JuneStatusReport2017-EN.pdf). Fortunately, such trends have been accompanied by a growing research interest in the field of humanitarian logistics that investigates mechanisms which can improve assistance to disaster-affected communities and thus minimize human suffering. In spite of acknowledging a major difference between such an objective and the priorities of business logistics, many authors still adopt disaster relief problem formulations that aim to minimize costs. In this paper, we list a number of issues with the cost-minimizing approach, placing emphasis on the significant challenge of determining the controversial economic value of human suffering that is usually a part of such formulations. These issues can easily be circumvented by the alternative formulations that maximize response, i.e., minimize unmet demand directly. The aim of our study is to investigate if cost-minimizing formulations are ever more suitable to find good emergency strategies than the the alternative models that minimize unmet demand. The discussion about the two approaches is illustrated with the problem of increasing emergency preparedness by pre-positioning relief items at strategic locations. We evaluate the two formulations of the pre-positioning problem using a number of randomly generated instances and a case study focused on hurricane threat in the Gulf Coast area of the United States. The optimal solution of the model that minimizes unmet demand always meets at least the same percentage of demand as the cost-minimizing model, and is obtained in comparable computation time. Our study therefore suggests that putting a price on human life can and ergo should be avoided. [ABSTRACT FROM AUTHOR]

Disaster management is a critical component to secure citizen's safety. From experiences with recent disasters such as tsunamis, earthquakes, and hurricanes, we can easily find evidence that shows an urgent need for effective disaster management systems. Most of existing disaster-responsive systems have focused on long-term needs that require comprehensive data analysis and continuous disaster recovery in a centralized manner. For more effective responses, however, a disaster response system must address not only long-term needs but also short-term needs that require instant data sharing and cooperation between people and smart devices nearby. Towards this goal, in this paper, we propose the Hybrid Disaster Response System (HyDRS) that consists of localized communities dealing with short-term needs and a centralized disaster management system addressing long-term needs. By using the HyDRS, distressed people are able to immediately obtain vital information from people and sensors nearby and cooperate with each other within localized communities even when a centralized system is unavailable. [ABSTRACT FROM AUTHOR]

The ice disaster risk in Ning-Meng reach of Yellow River in China is affected by many factors, and the various risk factors are not independent with each other but present complicated causal relationships. Identifying key ice disaster risk factors and analyzing the inner dependencies between factors are conducive to the successful implementation of ice disaster risk management. This paper concludes ice disaster risk factors through the current literature and the formation mechanism of ice disaster. The grey rough DEMATEL method is proposed to evaluate the interactions between risk factors, and it integrates the strength of grey number in characterizing decision maker's subjective information and the merit of rough logic in excavating and utilizing potential group decision information. The results show that the obtained visualized structural model can reveal the complex causal interactions between ice disaster risk factors effectively and determine the importance of risk factors. The study enhances ice disaster risk identification and provides a better reference for ice disaster prevention and management. [ABSTRACT FROM AUTHOR]

Rescue is a tough and time-consuming task in huge disaster. After stricken by a catastrophic natural disaster, the rescue force is typically deployed under the circumstance of traffic paralysis and lack of supplies. To cope with this situation, plenty of human resource and professional equipment are required. However, both the human resource and equipment are insufficient in most situations. The rescue force needs an efficient technology for searching the survivals, such as the life detection, which can be a key factor in disaster relief. Fast and accurate locating of the survivals will guide the limited rescue resource to save more survivors. However, current life detection technology cannot meet the demand of rescue in huge disaster. A novel life detection system based on fake base station is proposed. The detection system can scan the whole disaster area and find the cell phones, which leads to the surviving owners. As the system works in different way with other traditional human localization technology, a novel RSS/AOA hybrid positioning algorithm was proposed in this paper to increase the accuracy of localization. To verify the accuracy, two simulation tests were designed. The result of the two tests illustrates that the novel algorithm is more suitable to work with the novel life detection system. [ABSTRACT FROM AUTHOR]

This paper addresses questions raised by Storr, Haeffele-Balch and Grube in their book Community Revival in the Wake of Disaster: Lessons in Local Entrepreneurship (2015). I review the reasons why the book is timely, especially the rising number and costs of disasters globally and the shifting norms on the role of government in disaster management. Social capital serves as a critical engine for resilience to crisis, and I move to demonstrate its importance in the mitigation and recovery stages. Then, I raise a research agenda based on their writings, focusing on building quantitative evidence to match the qualitative data already gathered by the authors. Specifically, I focus on testing claims about the definition and density of entrepreneurs, the role of polycentricity, and the public policy pools that would build entrepreneurial talent in vulnerable communities. [ABSTRACT FROM AUTHOR]

Timely, effective delivery of relief resources to the sufferers in a natural disaster is quite crucial. All governments especially those having high risk of natural disasters must have a comprehensive and implementable plan to suitably manage humanitarian issues after a natural disaster occurs. They should construct sufficient facilities and prepare and store enough relief items (e.g., tents, medical packages, and meals) to be able to quickly respond to necessary needs of injured and homeless people; otherwise, it might lead to humanitarian crises with high numbers of casualties. In this study, we extend the mathematical two-stage stochastic optimization model proposed by Mete and Zabinsky (Int J Prod Econ 126:76-84, 2010) simultaneously considering two phases: pre- and post-disaster. All decisions pertinent to the pre-disaster phase, such as the locations of warehouse and pre-positioned relief items, should be taken in the first stage; however, other decisions pertinent to the post-disaster phase, such as the allocations of warehouses to the demanding points are taken in the second stage. In this study, we consider bi-directional relations between warehouses, which can increase the flexibility of the constructed network to handle the needs of injured people in a shorter time interval. Furthermore, whereas the relief process should be implemented within a specific time interval (e.g., 3 days), our extended optimization model is constructed for a multi-period and multi-product situation. We construct the extended model based on a professional report prepared by the Japanese International Cooperation Agency in a study of the seismic micro zoning of Tehran on April 13, 1999. The respective data in this report has also been updated based on the reports of Tehran Municipality to be matched with the current situation of Tehran. The results of our extended multi-period model for the respective real case study in this paper verify suitable responses and better services to the affected areas compared to what are provided through the respective single-period optimization model. [ABSTRACT FROM AUTHOR]

Crowdsourcing platforms for disaster management have drawn a lot of attention in recent years due to their efficiency in disaster relief tasks, especially for disaster data collection and analysis. Although the on-site rescue staff can largely benefit from these crowdsourcing data, due to the rapidly evolving situation at the disaster site, they usually encounter various difficulties and have requests, which need to be resolved in a short time. In this paper, aiming at efficiently harnessing crowdsourcing power to provide those on-site rescue staff with real-time remote assistance, we design and develop a crowdsourcing disaster support platform by considering three unique features, viz., selecting and notifying relevant off-site users for individual request according to their expertise; providing collaborative working functionalities to off-site users; improving answer credibility via 'crowd voting.' To evaluate the platform, we conducted a series of experiments with three-round user trials and also a System Usability Scale survey after each trial. The results show that the platform can effectively support on-site rescue staff by leveraging crowdsourcing power and achieve good usability . [ABSTRACT FROM AUTHOR]