Efficient Disaster Response Research Articles

Forecasting and stock control for fast-moving humanitarian aid supplies

In Indonesia, effective disaster management is imperative due to the region's susceptibility to a wide array of natural and man-made disasters. The establishment of the National Disaster Management Agency (BNPB) underscores the significance of this issue. This study investigates the improvement of disaster logistics, with a particular emphasis on the challenges associated with fast-moving humanitarian supplies. The primary obstacle in disaster logistics lies in the unpredictability of demand after the disaster occurred, coupled with the potential for supply disruptions stemming from inadequate inventory in warehouses. A critical focus of this research is placed on fast-moving humanitarian aid supplies, which are essential for immediate relief efforts, even when lead times are short. The study's primary objective is the development of a safety stock level policy for BNPB's national warehouse, adjusted through the analysis of demand forecasts and lead times. Notably, the research results for the year 2022 reveal minimal errors in demand forecasts. This outcome highlights the effectiveness of the safety stock policy in proactively addressing spikes in demand and mitigating uncertainties related to supply timelines. The research underscores the critical role of accurate inventory management, particularly in the case of fast-moving humanitarian supplies, within the context of disaster relief efforts. It emphasizes the importance of forward-thinking planning to meet urgent needs, ultimately contributing to more efficient disaster response efforts and ensuring the timely delivery of vital relief items to affected populations.

Development of measured hydrodynamic information-based flood early warning system for small streams

In general, small stream basins, characterized by narrow channels and steep slopes, face heightened vulnerability to climate change-induced flooding, posing challenges for evacuation procedures. With the increasing intensity of floods and typhoons in recent years, urgent measures are necessary to mitigate damage in such areas. This research endeavors to address these challenges by developing a novel small stream flood early warning system (SSFEWS) tailored to small streams and piloting its application. The proposed system integrates real-time hydrodynamic data collection, flood probability forecasting, and proactive warning issuance through an amalgamation of IoT-based sensor networks, statistical models leveraging measurement data, a robust constrained nonlinear optimization algorithm (RCNOA), and four-parameter logistic method (4PL). Moreover, system accuracy and reliability are enhanced by an automated iterative process that continuously refines forecasting model parameters via a user-defined rainfall-discharge nomograph and rating curve using RCNOA and 4PL. The developed SSFEWS is expected to contribute to the safety of the community as well as prevent possible small stream-related casualties by enabling efficient disaster response.© 2024 Elsevier Ltd. All rights reserved.

Blockchain Based Donation Management in Disaster Response

Both natural and man-made disaster leave thousands of people in vulnerable and in need for essential aid. While individuals generously donate resources, traditional donation management systems suffer from limitations. Centralized control, opaque transactions, and potential corruption often hinder aid delivery and leave victims in despair. This paper proposes a novel Ethereum blockchain-based system for transparent and secure disaster donation management. Utilizing smart contracts, the system ensures traceability, accountability, and immutability of donations, empowering donors and fostering trust. This paper presents the system's architecture, detailing its components and interactions through sequence diagrams and algorithms. Additionally, successful testing on the Sepolia Ethereum testnet validates its functionality. To assess its effectiveness, a cost and security analysis is conducted. This blockchain-based framework offers a promising solution for transparent and efficient disaster response, potentially revolutionizing donation management. Further research, particularly on donation allocation optimization within the system holds immense potential for future development.

Zero-shot knowledge transfer for seismic damage diagnosis through multi-channel 1D CNN integrated with autoencoder-based domain adaptation

Accurate and timely structural damage diagnosis is crucial to efficient disaster response and city renovation in post-earthquake events. The scarcity of labeled data hinders the powerful deep learning techniques from in-domain damage detection on target structures. Cross-domain transfer learning has emerged as a captivating strategy through digging knowledge from the abundant source domain to detect the damage in the target domain. However, the heterogeneity among multi-domain structures poses the challenge in seismic damage diagnosis. This study proposes a novel zero-shot knowledge transfer approach for seismic damage diagnosis through multi-channel one-dimensional convolutional neural networks (1D CNN) integrated with deep autoencoder (DAE)-based domain adaptation (DA). The framework consists of three modules, namely, data preprocessor adaptive to seismic vibration signals, DAE-based DA module, and damage diagnosis via multi-channel 1D CNN. The DA module is customized to seamlessly translate the unseen target-domain data to mimic latent representation via a DAE pretrained on the source data, thus realizing rigorous zero-shot learning. Imbalanced data distribution is also considered during the network training and testing. Two representative phases of knowledge transfer are performed to substantiate the knowledge transferability of the proposed method. The first phase involves multi-class damage quantification on two ASCE benchmark models from the simplified model to the refined one, and the second phase conducts binary damage detection on a three-story reinforced frame structure from the finite element numerical model to the laboratory-tested physical model. Both examples show that the proposed method exhibits high prediction accuracy and a lower false negative rate in achieving zero-shot knowledge transfer for cross-domain structural damage diagnosis. With a delicate network design for diverse data, the proposed knowledge transfer framework can be further extended from the present zero-shot approach to the few-shot learning paradigm, thus suggesting a feasible algorithm adaptability and promising engineering applicability.

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. These quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Impact of Media Information on Social Response in Disasters: A Case Study of the Freezing-Rain and Snowstorm Disasters in Southern China in 2008

Disaster information content is an objective mapping of disaster situations, social response, and public opinions. Social response to emergency is an important mechanism for implementing and guaranteeing emergency management of major natural hazard-related disasters. Understanding how disaster information content affects social response to emergencies is helpful for managing risk communication and efficient disaster response. Based on the 2008 freezing-rain and snowstorm disasters in southern China, this study used Python to extract 7,857 case-related media reports and applied natural language processing for text analysis. It used three typical cases to identify and analyze disaster media report content and the relationship between these reports and the social response to the emergency. Eight categories of disaster response—such as prewarning and forecasting, announcements by the authorities, and social mobilization—appeared in the disaster information in the media, along with disaster impact information, that is, real-time disaster status. Disaster response information and an appropriate amount of disaster impact information played important roles in prewarning, disaster relief, public opinion guidance, and social stability maintenance and can serve important functions in communicating with all stakeholders of emergency management, assisting or influencing emergency departments or individuals in decision making, and eliminating “information islands.” Empathy caused the general public to become “disaster responders” through receiving information. Rumors and an excess of negative information may have a perverse amplification effect on public opinion and increase the unpredictability of the disaster situation and the risk of social crisis.

The Resilience and Preparedness of Nurses in Disasters

Introduction: Indonesia's vulnerability to disasters highlights the urgent need for efficient disaster response. While nurses play a pivotal role, there exists a notable gap in their preparedness and the influence of resilience on their readiness. Purpose: This study aimed to investigate the relationship between resilience and disaster preparedness among nurses, exploring how various resilience dimensions impact their readiness for disaster. Methods: Between April and June 2023, 260 nurses from two naval hospitals in Jakarta were surveyed in a cross-sectional study. Participants were selected based on quota sampling and required a permanent registered nurse and minimum nursing diploma. The study utilized the Disaster Adaptation Resilience Scale (DARS) and the Disaster Preparedness Evaluation Tool (DPET) for data collection, analyzed using SPSS v26.0. Variables were categorized as low, medium, or high, with the Chi-Square and Fisher exact test evaluating variable relationships. Results: Of the 260 surveyed nurses, most were female diploma holders over 40 years of age, with over 11 years of experience. The average resilience score was 336.16 (SD=26.575). Resilience sub-components: Physical resources at 41.57 (SD=4.091) and social resources at 37.37 (SD=3.671). Disaster preparedness averaged 218.18 (SD=24.238). The total Resilience was significantly correlated with Disaster Preparedness (p=0.000, OR=0.323). Each resilience dimension was consistently aligned with the heightened disaster preparedness levels. Conclusion: The correlation between resilience and readiness emphasizes the need for holistic nursing training and revised policies to bolster crisis responses.

Integrated optimization of facility location, casualty allocation and medical staff planning for post-disaster emergency response

PurposeDisaster management and humanitarian logistics (HT) play crucial roles in large-scale events such as earthquakes, floods, hurricanes and tsunamis. Well-organized disaster response is crucial for effectively managing medical centres, staff allocation and casualty distribution during emergencies. To address this issue, this study aims to introduce a multi-objective stochastic programming model to enhance disaster preparedness and response, focusing on the critical first 72 h after earthquakes. The purpose is to optimize the allocation of resources, temporary medical centres and medical staff to save lives effectively.Design/methodology/approachThis study uses stochastic programming-based dynamic modelling and a discrete-time Markov Chain to address uncertainty. The model considers potential road and hospital damage and distance limits and introduces an a-reliability level for untreated casualties. It divides the initial 72 h into four periods to capture earthquake dynamics.FindingsUsing a real case study in Istanbul’s Kartal district, the model’s effectiveness is demonstrated for earthquake scenarios. Key insights include optimal medical centre locations, required capacities, necessary medical staff and casualty allocation strategies, all vital for efficient disaster response within the critical first 72 h.Originality/valueThis study innovates by integrating stochastic programming and dynamic modelling to tackle post-disaster medical response. The use of a Markov Chain for uncertain health conditions and focus on the immediate aftermath of earthquakes offer practical value. By optimizing resource allocation amid uncertainties, the study contributes significantly to disaster management and HT research.

Analisis jejaring sosial gempa Cianjur di Twitter sebagai mitigasi dampak bencana

Indonesia is a country that has the potential for volcanic and tectonic earthquakes. One of the actions that can be taken to minimise the impact of disasters is to mitigate natural disasters through social media, such as Twitter. The #PrayForCianjur hashtag is one of the efforts to expand information by utilising Twitter to minimise the impact of the disaster in Cianjur as well as provide prompt action from related parties. This research aims to analyse the social network hashtag #PrayForCianjur, which became a topic of public discourse on Twitter after the Cianjur earthquake occurred. The study results show that the information centre actors are non-institutional actors such as @marchfoward, @aqfiazfan, @tanyakanrl, and @convomf. Meanwhile, institutional actors such as @nctzenhumanity, @detik.com, and @info_bmkg There are interesting findings in this research: actors who should be actively involved in disaster mitigation are not popular in the network. This study will operate as a foundation for providing the crisis management and mitigation teams with helpful information that they can use to prepare for and plan an efficient disaster response and to support the creation of automated crisis management systems in the future.

Flood or Non-Flooded: A Comparative Study of State-of-the-Art Models for Flood Image Classification Using the FloodNet Dataset with Uncertainty Offset Analysis

Natural disasters, such as floods, can cause significant damage to both the environment and human life. Rapid and accurate identification of affected areas is crucial for effective disaster response and recovery efforts. In this paper, we aimed to evaluate the performance of state-of-the-art (SOTA) computer vision models for flood image classification, by utilizing a semi-supervised learning approach on a dataset named FloodNet. To achieve this, we trained son 11 state-of-the-art (SOTA) models and modified them to suit the classification task at hand. Furthermore, we also introduced a technique of varying the uncertainty offset λ in the models to analyze its impact on the performance. The models were evaluated using standard classification metrics such as Loss, Accuracy, F1 Score, Precision, Recall, and ROC-AUC. The results of this study provide a quantitative comparison of the performance of different CNN architectures for flood image classification, as well as the impact of different uncertainty offset λ. These findings can aid in the development of more accurate and efficient disaster response and recovery systems, which could help in minimizing the impact of natural disasters.

Complex disaster response framework to reduce urban disaster vulnerability.

Cities are vulnerable to a range of disasters that can occur simultaneously due to their complexity. Therefore, an effective disaster response plan is needed to reduce the disaster vulnerability of cities. In particular, evacuation route management is important for reducing the losses from a disaster. Efficient disaster response can be realized by searching for suitable evacuation routes and effective road network management. In this paper, we propose a disaster response framework based on a multilayered road network structure and evacuation routes based on our road network. The suggested road structure consists of three layers for the effective management of the network. An A* algorithm-based search for multiple evacuation routes under different conditions in response to an individual disaster on the configured road map provides a safe route for evacuees. As a result, the damage caused by disasters in urban areas can be ameliorated.

A Framework for Burnt Area Mapping and Evacuation Problem Using Aerial Imagery Analysis

The study aims to develop a holistic framework for maximum area coverage of a disaster region during a bushfire event. The monitoring and detection of bushfires are essential to assess the extent of damage, its direction of spread, and action to be taken for its containment. Bushfires limit human’s access to gather data to understand the ground situation. Therefore, the application of Unmanned Aerial Vehicles (UAVs) could be a suitable and technically advanced approach to grasp the dynamics of fires and take measures to mitigate them. The study proposes an optimization model for a maximal area coverage of the fire-affected region. The advanced Artificial Bee Colony (ABC) algorithm will be applied to the swarm of drones to capture images and gather data vital for enhancing disaster response. The captured images will facilitate the development of burnt area maps, locating access points to the region, estimating damages, and preventing the further spread of fire. The proposed algorithm showed optimum responses for exploration, exploitation, and estimation of the maximum height of the drones for the coverage of wildfires and it outperformed the benchmarking algorithm. The results showed that area coverage of the affected region was directly proportional to drone height. At a maximum drone height of 121 m, the area coverage was improved by 30%. These results further led to a proposed framework for bushfire relief and rescue missions. The framework is grounded on the ABC algorithm and requires the coordination of the State Emergency Services (SES) for quick and efficient disaster response.

Measure of Utilizing Space Database Information for Improvement of Efficient Disaster Management (Focusing on Nuclear Power Plant Accidents)

The damage caused by disasters is increasing worldwide, with hundreds of thousands of deaths due to the occurrence of complex large-scale disasters such as the 2010 Haiti earthquake and the 2004 Indian tsunami. South Korea has also experienced human casualties and damage to property caused by large-scale disasters in the past 10 years. Accordingly, a disaster-appropriate response measure is needed. Thus, we conducted this study to present a measure of utilizing spatial database and image information to improve the efficiency of disaster management that is operated based on the country’s existing national disaster management system. We present an efficient disaster response measure that differs from the existing collection-, reporting-, and propagation-oriented operating methods of disaster information through the use of spatial database and image-based information that can be combined with mandatory information with regard to nuclear power plant accidents. Thus, this study contributes to deriving a system that could collect and provide information rapidly at the time of disaster by defining the attribute and spatial information required at the time of disaster during nuclear power plant accidents and by deriving available systems and providing institutions.

Domestic application of lessons learned by Canadian health care professionals working in international disaster settings: a qualitative research study.

Background:Individuals with prior experience in international disaster response represent an essential source of expertise to support disaster response in their home countries. Our objective was to explore the experiences of personnel involved in international emergency health response regarding their perceptions of essential disaster response attributes and capacities and determine how these competencies apply to the Canadian context.Methods:For this qualitative study, we conducted semistructured interviews with key informants in person or over the telephone from May to December 2018. Participants were delegates deployed as part of the Canadian Red Cross medical response team in a clinical or technical, or administrative role within the last 5 years. Interviews were audio-recorded and transcribed. Conventional content analysis was performed on the transcripts, and themes were developed.Results:Eighteen key informants from 4 Canadian provinces provided perspectives on individual attributes acquired during international deployments, such as agility and stress management, and team capacities developed, including collaboration and conflict management. Key informants, including administrators (n = 5), technicians (n = 4), nurses (n = 4), physicians (n = 3) and psychosocial support workers (n = 2), described these experiences as highly relevant to the Canadian domestic context.Interpretation:Canadian physicians and health care workers involved with international disaster response have already acquired essential capacities, and this experience can be vital to building efficient disaster response teams in Canada. These findings complement the Canadian Medical Education Directives for Specialists (CanMEDS) roles and can inform course design, competency and curriculum development for physician and professional training programs related to disaster response and preparedness.

Sustainable Supply Chain Management Practices and Performance of Humanitarian Organizations in Nairobi County, Kenya

Humanitarian supply chain disaster response is core to alleviating communities economically, socially and in the environmental dimension. Stakeholders including donors, governmental and non-governmental organization, and supply aid beneficiaries in the past decade have witnessed a transformation in the humanitarian sector, despite the increased frequency and magnitude of disasters. These have been evident through the increased scholarly works together with collaboration of international agencies to respond to the needs and develop the sub-Saharan Africa region. Humanitarian supply chain sustainability, therefore, is at the forefront of effective and efficient disaster response. Studies done on impact of sustainable supply chain measures in Kenya have put the humanitarian organizations under strict scrutiny for accountability of donor aid and need for improved performance. These situations formed the foundation of the research study. The main objective of research was to determine sustainable chain management practices and the performance of humanitarian organizations in Nairobi County Kenya. The specific objectives adopted were to assess the effect of green procurement, green inventory management, green logistics and corporate social responsibility on the performance of humanitarian organizations in Nairobi County Kenya. Theories relevant to the study were reviewed and included supply chain operations reference model (SCOR), contingency theory, resource-based view theory and social network theory. The target population was employees of the 30 selected humanitarian organizations within Nairobi County Kenya. The study employed stratified random sampling and sample size was 162 respondents. The study employed the use of questionnaires for data collection which were self-administered by research assistants. Quantitative and qualitative data was sorted, coded and entered in Statistical Package for Social Sciences for analysis. Descriptive statistics were generated with tables and graphs used to present the outcomes. Correlation analysis was applied to test the relationship between variables where all the independent variables were positively correlated and significant association with the dependent variable. The study established that green procurement, green inventory management, green logistics, corporate social responsibility had a significant and positive effect on performance of humanitarian organizations in Nairobi County Kenya. The study recommends environmental policy should be a criterion during supplier selection and monitoring of the procurement processes; smart pre-selection of suppliers that are reliable to make supplier engagement manageable; organization put emphasis on adoption of sustainable logistical practices such as consolidation, fleet and fuel management. Finally, the study recommends setting of CSR policy and channels to communicate the policy on corporate social responsibility activities of the organization.

Improving Communication for Older Adults During Disaster Events

Management of any disaster event (i.e., hurricanes, flooding, wildfires, viral pandemic) is a complicated task. An important and frequently overlooked aspect of efficient disaster response is effective information exchange among emergency managers, aging and social service providers, and those impacted by the disaster. Breakdowns in communication are regularly cited as failures in actual emergency situations. Older adults have unique challenges to effective communication during a disaster event. Further, messaging platforms may be inaccessible to older adults. Given the lack of data collected on the role of effective communication for older adults in a disaster event, we developed this symposium to highlight lessons learned for facilitating disaster communication among older community members and discuss how policy can further disaster communication efforts. The first presentation highlights the advantages of using targeted marketing communication channels to encourage older adults to prepare and recover from a disaster, as well as the value of collaborative partnerships in this communication. The second presentation introduces results from research collected from older adults on their use of social media to stay informed before and after Hurricane Matthew. The third presentation explores whether assisted living communities in Florida’s affected counties evacuated or sheltered in place in the context of emergency management communications concerning evacuation. The fourth presentation highlights the experiences of older persons in Houston’s communication post-Hurricane Harvey from an aging service provider’s perspective. The final presentation discusses disaster literacy and how this model can be used to enhance outcomes at all phases of a disaster. Disasters and Older Adults Interest Group Sponsored Symposium.

EFFECTIVENESS OF TOT WORKSHOP ON PSYCHOSOCIAL CARE IN DISASTER MANAGEMENT FOR NATIONAL DISASTER RESPONSE FORCE PERSONNEL

Introduction: The efficient disaster response will depend primarily on effectiveness of training and re-training of specialized Disaster Response Forces in all dimensions of disaster management that includes psychosocial disaster preparedness. Therefore, NIMHANS and NIDM, collaboratively conducted ToT workshop on psychosocial care in disaster management for NDRF personnel. Methods: The study adopted quasi experimental design with post only research design. There were 20 participants who were part of the training program. The content of the program includes the disaster resilience, need, importance and techniques of psychosocial care; and role of the NDRF personnel in provision of psychosocial care in disaster management. A semi structured questionnaire consisting of socio demographic profile, Overall evaluation of workshop, psychosocial aspects in Disaster Management was developed for assessing the effectiveness of the program. Descriptive statistics was carried out to analyze the data. Results: Majority of the participants found the content of the program from very good to excellent, very well structured and organized. Overall impression of the program was reported as very good to excellent by most of the participants. Conclusion: Integration of psychosocial care services to NDRF personnel in disaster management would equip them to deal with the psychosocial issues effectively. There is also a need to train all the battalions of NDRF as an effective and efficient disaster response force that can deal with the immediate psychosocial impact on survivors of disasters.

Prioritizing distribution centers in humanitarian logistics using type-2 fuzzy MCDM approach

PurposeLocating disaster response centers is one of the key elements of efficient relief operations. The location and infrastructure of the candidate facilities usually conform to the required criteria at different levels. This study aims to identify the criteria for the main and local distribution center location problem separately and prioritize each candidate distribution center using a hybrid multiple criteria decision-making approach.Design/methodology/approachThe proposed model incorporates analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) under interval type-2 fuzzy sets (IT2FSs) to overcome the uncertainty of experts` judgments and expressions in the evaluations of candidate distribution centers. In the proposed approach, weights of the criteria are determined using type-2 fuzzy AHP and the candidate distribution centers are prioritized using type-2 fuzzy TOPSIS.FindingsTransportation, cost, infrastructure and security are determined as the main criteria for the main distribution center location criteria. Cost, warehouse facilities and security are the main criteria for local distribution center location selection. Prioritization enables decision-makers to assess each alternative accordingly to be able to select the best locations/facilities for efficient disaster response operations.Originality/valueThis study proposes new multi-criteria decision support models for prioritizing disaster response distribution centers. IT2FSs are used to be able to reflect both the complexity and vagueness of disaster environment and expert opinions. Different support models are suggested for main and local distribution centers considering their different missions. The proposed methodology is applied in Istanbul city, Turkey, where a high-magnitude earthquake is expected.

Crowd or Hubs: information diffusion patterns in online social networks in disasters

The objective of this paper is to investigate the role of different types of users in the diffusion of situational information through online social networks in disasters. In particular, this paper investigates the influence of two types of users: crowd (regular users) and hubs (users with a large number of followers) on the speed and magnitude of information propagation. Effective and efficient disaster response requires rapid dissemination of situational information to improve situation awareness, save lives, and quickly repair damages. The use of social media, such as Twitter, has gained popularity for spreading the situational information in disasters. Little is known, however, regarding the underlying diffusion mechanisms influencing the speed and magnitude of spreading disaster-related information on social media. To address this gap, using tweets related to Hurricane Harvey, this study examined the role of hubs and crowd and the influence of different features such as theme, hashtag, media, users’ location, and the intervention timing of online users on the speed and magnitude of information spread. The results compare the differences in the speed and magnitude of information spread between two diffusion patterns: crowd diffusion (less than 2% retweets from hubs) and mixed diffusion (more than 2% retweets from hubs). The findings suggest that both diffusion patterns can achieve high speed and high magnitude in terms of information diffusion for trending tweets with different features. For mixed diffusion, the speed and magnitude of the tweets are governed by the activities of hubs. The results also show that early intervention of hubs increases the speed of information propagation. Also, in the crowd diffusion, information spread is influenced by both crowd and hubs whose retweets cause tipping points in the information diffusion process at different points of time. The findings imply intervention strategies to better disseminate situational information in disasters.

Efficient Disaster Response Using Reserve Forces - Focusing on the US National Guard -

현재 우리가 살고 있는 사회는 기상이변으로 인한 자연재난과 산업발달로 인한 산업재난 등 크고 작은 재난이 끊임없이 발생하고 있다. 그리고 이러한 재난은 점점 대형화되어 이에 따른 사회적 피해와 복구비용도 천문학적으로 증가하고 있는 실정이다. 이와 같이 재난의 발생과 규모는 점차 증가하는데 비해, 재난에 대응하는 소방이나 지방자치단체의 능력은 한계성을 나타내고 있다. 이와같은 재난대처의 한계성은 엄청난 규모의 사회적 피해로 나타나며, 이러한 재난에 효율적으로 대처하기 위해서는 재난발생 초기에 신속한 재난대응과 피해에 대한 복구 활동이 필요하다. 하지만 재난대응을 위해 짧은 시간에 인원을 동원하기란 현실적으로 많은 어려움이 따르며, 이에 신속하게 동원할 수 있는 예비군조직을 활용한다면 증가하고 있는 재난에 대한 효율적인 대처방안이 될 것이다.The current society has experienced constantly small and large disasters including natural disasters (e.g. extreme weather) and industrial disasters due to industrial development. Moreover, the disasters are getting bigger and more serious and the social damage and recovery costs are increasing astronomically. Despite the increase of the occurrence and magnitude of various disasters, the ability of firefighting agencies and local governments to respond to disasters is still limited. Since the limited disaster response could cause enormous social damages, rapid and effective responses to cope with disaster and recover damages should be undertaken at the early stage of disaster cycle. However, mobilizing personnel to respond to disasters in a short period of time is a real challenge, and thus it would be an effective way to respond to a growing number of disasters using a reserve army which can be quickly mobilized.