Emergency Management Applications Research Articles

Efficient CNN-based low-resolution facial detection from UAVs

AbstractFace detection in UAV imagery requires high accuracy and low execution time for real-time mission-critical operations in public safety, emergency management, disaster relief and other applications. This study presents UWS-YOLO, a new convolutional neural network (CNN)-based machine learning algorithm designed to address these demanding requirements. UWS-YOLO’s key strengths lie in its exceptional speed, remarkable accuracy and ability to handle complex UAV operations. This algorithm presents a balanced and portable solution for real-time face detection in UAV applications. Evaluation and comparison with the state-of-the-art algorithms using standard and UAV-specific datasets demonstrate UWS-YOLO’s superiority. It achieves 59.29% of accuracy compared with 27.43% in a state-of-the-art solution RetinaFace and 46.59% with YOLOv7. Additionally, UWS-YOLO operates at 11 milliseconds, which is 345% faster than RetinaFace and 373% than YOLOv7.

A Proposal for a Federated Learning Protocol for Mobile and Management Systems

In this research paper, we introduce a federated learning communication protocol tailored for emergency management applications. Our primary objective is to tackle the communication challenges that arise in such critical scenarios. In order to overcome the limitations associated with centralized server architectures, we present an innovative communication protocol. This protocol empowers the framework to effectively cooperate with multiple centralized servers, fostering efficient knowledge sharing and model training while ensuring the utmost data privacy and security. By harnessing this protocol, our framework elevates the performance and resilience of vital infrastructure systems operating on the Android platform, thereby facilitating real-time operational scenarios. This research makes a substantial contribution to the field of emergency management applications, as we offer a comprehensive solution that optimizes communication and enables seamless collaboration with numerous centralized servers.

Automatic detection of crop lodging from multitemporal satellite data based on the isolation forest algorithm

Rapid identification of crop lodging has implications for optimizing disaster prevention and mitigation measures. This study proposes a sequentially integrated algorithm for crop lodging identification, which first automatically extracts lodged and non-lodged samples based on the isolation forest algorithm from the screened features dataset and then uses a supervised classifier for lodging identification. Taking the crop lodging disaster caused by the super typhoon “Maysak” in Bohetai Township as an example, we calculated 162 feature indices from Sentinel-1 and Sentinel-2 satellite data pre- and post-disaster and screened the optimal features. Then, the abnormal scores of the cultivated-land pixels were calculated using the isolation forest algorithm. After that, we calculated the coefficient of variation (CV) of the abnormal scores of each group, extracted the lodged and non-lodged samples based on the cumulative CV contribution rate, and input samples into three supervised classifiers (random forest, maximum likelihood, and support vector machine) to identify lodged crop pixels. The results showed that feature screening increased the identification accuracy of crop lodging by more than 10% and difference features of short-wave infrared (SWIR) and red edge bands contributed significantly to the identification. The accuracy of crop lodging automatic identification was verified using samples obtained from visual interpretation and digitization of UAV images as reference data and the overall accuracy reached 78%. The proposed method can simultaneously recognize lodging of different crop types without the need to select lodged samples and extract lodged ranges for each crop. With low data acquisition costs and high automation, the proposed method has promising applications in agricultural disaster emergency management and agricultural insurance loss assessment.

A Framework for a Mobile Knowledge Management Application for Crisis and Emergency Management

Abstract The widespread availability of mobile devices offers enormous opportunities for first responders to have instant access to sources of information, and collaborate with one another when responding to an emergency. Although various knowledge management systems have been developed in different domains, no mobile knowledge management application so far has been developed in the area of emergency or crisis management. This study addresses various technical requirements for a mobile knowledge management application specifically designed for first responders to an emergency or crisis.

The cycle of disparity: A gap analysis of emergency management and information technology.

Emergency management (EM) requires an appropriate understanding of its situational context in real time. This involves constant communication between emergency operations and administration, so that informed, coordinated decisions can be made in a constantly changing environment using the most critical and updated information. Challenges in EM include adapting to rapidly changing technologies, adoption, and communication. The goal of this needs assessment is to examine how the effective collection and communication of information has been affected by the technology developed to support it, to identify critical disconnects between emergency management professionals and information technology and telecommunications (ITT) professionals, and to identify opportunities for improved application and user interface development. The methods for this qualitative analysis enumerate and categorize some of the communication challenges faced by the EM community as identified by interviews conducted with subject matter experts using the strengths, weaknesses, opportunities, and threats technique. This study finds that the absence of ITT professionals with experience in emergency response and, conversely, the absence of EM professionals working in EM application development create a "cycle of disparity," where the key findings of this study both arise from one central input and feed into to each of the other key findings in a continuous cycle, reinforcing what threats and weaknesses already exist.

Use of low-cost online tools to estimate the public's interest in the changing conditions of their existence under the conditions during COVID-19, as a model for the emergency information assessment.

Online tools such as Google Trends and search volume trackers provide potential low-cost methods to evaluate the interest of the public in the current topics once a disaster strikes. A low-cost plugin is available commercially to extract the public interest data from Google and it is tested in this letter to the editor. The plugin was used to assess the time trends in the South African public's interest in working from home which became a relevant topic during the COVID-19 lockdowns. Results of the data extraction and analysis are presented and potential applications in emergency management are proposed.

A Review of Digital Twin Applications in Civil and Infrastructure Emergency Management

Natural disasters can cause severe damages to civil infrastructure and lead to extensive economic losses and casualties. To improve the emergency response capability of civil infrastructure under extreme circumstances such as natural disasters and human-caused hazards, intelligent technology for infrastructure emergency management has been extensively studied. As an emerging paradigm of interdisciplinary convergence, digital twins (DTs) can integrate intelligent technology into different stages of emergency management and provide a new solution for the emergency management of civil infrastructure (EMCI). However, applications of DT in EMCI have several limitations and are mostly case by case. However, the sector needs more generalisable lessons to address the greater value of DT in the context of EMCI. To address this gap, we first carry out a systematic literature review and analyse the latest progress and previous research deficiencies of DT by taking the scientometrical approach. Next, a framework is proposed to explain how DT can be applied to the mitigation, preparation, response, and recovery stages of EMCI. Lastly, the trends and prospects of DT applications in EMCI are discussed. Overall, the knowledge gained from this study will promote the research and development of more-viable DTs to address the sector’s demand for emergency management.

A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment

The level of destruction caused by an earthquake depends on a variety of factors, such as magnitude, duration, intensity, time of occurrence, and underlying geological features, which may be mitigated and reduced by the level of preparedness of risk management measures. Geospatial technologies offer a means by which earthquake occurrence can be predicted or foreshadowed; managed in terms of levels of preparation related to land use planning; availability of emergency shelters, medical resources, and food supplies; and assessment of damage and remedial priorities. This literature review paper surveys the geospatial technologies employed in earthquake research and disaster management. The objectives of this review paper are to assess: (1) the role of the range of geospatial data types; (2) the application of geospatial technologies to the stages of an earthquake; (3) the geospatial techniques used in earthquake hazard, vulnerability, and risk analysis; and (4) to discuss the role of geospatial techniques in earthquakes and related disasters. The review covers past, current, and potential earthquake-related applications of geospatial technology, together with the challenges that limit the extent of usefulness and effectiveness. While the focus is mainly on geospatial technology applied to earthquake research and management in practice, it also has validity as a framework for natural disaster risk assessments, emergency management, mitigation, and remediation, in general.

Machine Learning for Emergency Management: A Survey and Future Outlook

Emergency situations encompassing natural and human-made disasters, as well as their cascading effects, pose serious threats to society at large. Machine learning (ML) algorithms are highly suitable for handling the large volumes of spatiotemporal data that are generated during such situations. Hence, over the years, they have been utilized in emergency management to aid first responders and decision-makers in such situations and ultimately improve disaster prevention, preparedness, response, and recovery. In this survey article, we highlight relevant work in this area by first focusing on the commonalities of emergency management applications and key challenges that ML algorithms need to address. Then, we present a categorization of relevant works across all the emergency management phases and operations, highlighting the main algorithms used. Based on our review, we conclude that ML algorithms can provide the basis for tackling different activities across the emergency management phases with a unified algorithmic framework that can solve a large set of problems. Finally, through the systematic literature review, we provide promising future directions for utilizing ML algorithms more effectively in emergency management applications. More importantly, we identify the need for better generalization of algorithms, improved explainability, and trustworthiness of ML algorithms with respect to the emergency management personnel, as well as more efficient ways of addressing the challenges associated with building appropriate datasets.

Research on public satisfaction of government typhoon emergency management under artificial intelligence: An empirical analysis based on Xuwen County

<abstract> <p>Typhoon natural disasters belong to one of the four major categories of public safety events. Typhoons have stochastic uncertainty and dynamic complexity, and frequent typhoons often cause heavy casualties and property losses in China's coastal areas, seriously affecting economic development and social stability. With the rapid development of artificial intelligence (AI) technology, intelligent disaster prevention and mitigation will become the trend of future development and a hot spot for research. Based on reviewing the current situation and trend of development, this study compares and analyzes the public satisfaction of communities using traditional technology methods and AI technology applications in typhoon disaster emergency management by constructing a public satisfaction model through the literature review, taking Xuwen County, China, as an example. The study shows that AI technology has an important role in the 3 main aspects of early identification, risk assessment, risk prevention and control, and provides a new technical approach to typhoon disaster emergency management. Finally, we propose the construction scheme of the typhoon emergency management system based on AI.</p> </abstract>

The scholarship of teaching and learning: Understanding the concept and its application in academic programs and emergency management.

For years, education has gone through transformation and incorporated innovation to improve the effectiveness of teaching and learning. The scholarship of teaching and learning (SoTL) is one such activity that transforms and innovates education to engineer effectiveness. SoTL is internationally accepted and applied in Education, Psychology, Management Science, Chemistry, History, English, Sociology, Communication Studies, Mathematics, Engineering, Medicine, and Interdisciplinary Programs with a growing interest in Emergency Management and Homeland Security (EMHS). In relation to the EMHS programs, the Federal Emergency Management Agency (FEMA) SoTL Special Interest Group members have participated in the American Educational Research Association annual meetings and then shared the relevance of the SoTL concept with the EM scholars, researchers, and practitioners at the FEMA Higher Education programs.1-4 Thus, the concept of SoTL cuts across academic barriers with methodological pluralism in educational practice. In addition, the success of SoTL has been driven with the publication of two edited volumes of Carnegie Scholars' works: (1) Opening Lines: Approaches to the Scholarship of Teaching and Learning5 and (2) Disciplinary Styles in the Scholarship of Teaching and Learning: Exploring Common Ground.6 However, even with these advances, there are some concerns about SoTL critiques. One such critique comes from Boshier who asserts that SoTL is a difficult academic concept to be accepted by the academic community.7 Criticisms -notwithstanding, there are many benefits due to SoTL. Its future is bright and encouraging as nations benefit from SoTL's educational transformative and innovative power.

TENG Applications in Transportation and Surrounding Emergency Management

AbstractWith the rapid development of transportation technology, people's lives are becoming more and more convenient, but the popularity of high speed transportation also increases the probability of emergencies. Therefore, it is necessary to conduct real‐time and accurate monitoring on transportation conditions to reduce the occurrence of emergencies. For possibly emergencies in other circumstances (e.g., meteorological disasters, geological disasters, and maritime disasters), emergency management is also critical to ensure the security of people and property. Prediction and early‐warning can help to detect abnormal situations and take measures, prior to potential risks. The basis of real‐time and effective transportation monitoring and emergency management requires a continuous and stable power supply for randomly distributed sensors and actuators. Triboelectric nanogenerators (TENGs) have been extensively investigated in different transportation circumstances to harvest the surrounding energy and drive distributed sensors for self‐powered wireless monitoring. This article surveys the research progress in TENGs in road transportation, bridge transportation, railway transportation, and maritime transportation, and related surrounding emergency management. Representative applications and the main achievements are summarized for these different application scenarios. A perspective on TENG research is given, and the remaining challenges facing TENGs in transportation and emergency management are discussed, along with potential solutions.

Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

Enhanced intuitionistic fuzzy similarity operators with applications in emergency management and pattern recognition

Maximum-Entropy-Based Decision-Making Trial and Evaluation Laboratory and Its Application in Emergency Management

Decision-making trial and evaluation laboratory (DEMATEL) can visualize the structure of complex causal relationships, so it is widely used in decision making. One of the important steps in DEMATEL is normalization, and it has received much attention in recent years. Maximum entropy (MaxEnt) is a universal principle, and it is an effective tool for determining the amount of information existed in evidence. In this paper, the authors propose MaxEnt-based DEMATEL. The greatest contribution in this paper is the use of MaxEnt principle to determine the normalized direct influence matrix, which allows to obtain the normalized matrix with minimal information loss. The authors illustrate emergency management to show the superiority of the proposed method.

Standardization and Interoperability of Small Unmanned Aircraft Systems (sUAS) for Disaster Management

Abstract There are many recognized use cases for small unmanned aircraft systems (sUAS) in emergency and disaster management, but there are obstacles to the full realization of this technology’s potential. More stringent training and certification requirements for public safety use of sUAS is a crucial step in achieving standardization of operations, a common operational picture, interoperability, increased airspace safety, and public acceptance. The purpose of this study is to move toward standards for public safety sUAS pilot training requirements. This research employs a mixed methods approach to investigate the current state of training requirements and trends in public safety sUAS programs. This included a review of the relevant literature, an analysis of survey data, and a combination of structured and semi-structured interviews. While the literature clearly shows many sUAS emergency management applications, it also identifies dangers that must be mitigated. Synthesizing the literature findings with the survey and interview data resulted in four categories of possible sUAS training standard recommendations: progressive levels of certification, flight time minimums, task-focused practical training, and proficiency demonstrations. These recommendations have both national and international applicability.

Blockchain: A potential technology to improve the performance of collaborative emergency management with multi-agent participation

As the complexity of emergencies continues to increase, responding to them often exceeds the capabilities of any single public agency, multi-agent collaborative participation is an inevitable tendency in the development of emergency management. In the collaborative emergency response, it is important to support various responders with a unified platform to assist in decision-making, distribution of resources, and coordination of emergency operation. In recent years, blockchain has provided strong support for the innovation of emergency management mechanisms with its technical advantages such as distributed storage, decentralization, consensus algorithms, tamper-proof, traceability, and smart contract. This study firstly reviews the development of blockchain and its application in emergency management. Secondly, the connotation and key dilemmas of the multi-agent collaborative emergency management mechanism are expounded from two aspects of the multi-agent participation and the construction of a collaborative mechanism. Then, the possibility of using blockchain to facilitate the innovation of multi-agent collaborative emergency management mechanism is analyzed from the aspects of emergency management information sharing, resource storage and distribution, the rights and obligations of multiple subjects, supervision, reward and punishment. Finally, a conceptual model of a multi-agent collaborative emergency management mechanism based on alliance chain and public chain is proposed. The application of blockchain provides a new perspective and motivation for the innovation of emergency management system. This study provides a new method for improving the multi-agent collaborative emergency management mechanism, and is an important supplement and innovation to existing emergency management studies.

An Intelligent Proposed Model for Task Offloading in Fog-Cloud Collaboration Using Logistics Regression.

Smart applications and intelligent systems are being developed that are self-reliant, adaptive, and knowledge-based in nature. Emergency and disaster management, aerospace, healthcare, IoT, and mobile applications, among them, revolutionize the world of computing. Applications with a large number of growing devices have transformed the current design of centralized cloud impractical. Despite the use of 5G technology, delay-sensitive applications and cloud cannot go parallel due to exceeding threshold values of certain parameters like latency, bandwidth, response time, etc. Middleware proves to be a better solution to cope up with these issues while satisfying the high requirements task offloading standards. Fog computing is recommended middleware in this research article in view of the fact that it provides the services to the edge of the network; delay-sensitive applications can be entertained effectively. On the contrary, fog nodes contain a limited set of resources that may not process all tasks, especially of computation-intensive applications. Additionally, fog is not the replacement of the cloud, rather supplement to the cloud, both behave like counterparts and offer their services correspondingly to compliance the task needs but fog computing has relatively closer proximity to the devices comparatively cloud. The problem arises when a decision needs to take what is to be offloaded: data, computation, or application, and more specifically where to offload: either fog or cloud and how much to offload. Fog-cloud collaboration is stochastic in terms of task-related attributes like task size, duration, arrival rate, and required resources. Dynamic task offloading becomes crucial in order to utilize the resources at fog and cloud to improve QoS. Since this formation of task offloading policy is a bit complex in nature, this problem is addressed in the research article and proposes an intelligent task offloading model. Simulation results demonstrate the authenticity of the proposed logistic regression model acquiring 86% accuracy compared to other algorithms and confidence in the predictive task offloading policy by making sure process consistency and reliability.

Shared Decision Making

Shared decision making (SDM) is a process by which physicians and patients jointly participate in choosing to pursue one of several alternatives in a clinical decision. It is most relevant for decisions that involve significant potential harms and benefits with tradeoffs and uncertainty. This paper provides a state-of-the-art review about SDM covering its concept, value, implementation and application in emergency management and communication. SDM is valuable in the process of making decisions which patients may benefit most from, thus resulting in satisfying patient-centered outcomes. Although SDM can be challenging to incorporate into clinical practice, it is likely to become a useful tool of communication in future.

A new fuzzy multi-attribute group decision-making method with generalized maximal consistent block and its application in emergency management

Decision-making is the most important business activity and becomes more complex in the current big-data situation. Most organizational decision-making is made in a group and has the data analytics function to seek specific answers for specific purposes. Multi-attribute group decision-making (MAGDM) methods provide effective support to decision groups by evaluating and integrating individual group members opining. However, current MAGDM methods often suffer from the problem of opining data and decision environment uncertainty, which is particularly severe in a large decision group or a newly decision problem. As a solution, rough sets and fuzzy sets have been applied in MAGDM to deal with data and decision process uncertainties. Although a lot of efforts have been made in applying rough sets and fuzzy sets to deal with data and decision process uncertainties the area, the disadvantage of rough set models in classification accuracy when similar class as basic knowledge granularity has not been well solved yet. This paper aims to solve this problem by introducing a new concept-maximal consistent block (MCB) and multi-granulation decision-theoretic rough set (MCB-MDTRS) models. Firstly, it establishes a binary tolerance relation on the universe of discourse, defines generalized MCBs, and introduces pessimistic DTRS based on MCBs. Secondly, it extends objective set to a fuzzy environment and proposes four MCB-MDTRFS models with consideration of the weight of each attribute. The steps of the proposed fuzzy MAGDM methods are carefully described in detail. Different from existing fuzzy MAGDM methods, the weight of each attribute is considered in the determination of the positive ideal decision objective and negative ideal decision objective in this paper, named by qualified and unqualified (fuzzy) sets. We solve the vague problem of individual preference evaluation and the uncertainty of setting ideal decision objectives by using the advantages of fuzzy set and rough set theory. Finally, it takes emergency plan selection as a case study to analyze the effectiveness of our methods and compare with other fuzzy MAGDM methods. Our methods outperform the selection of basic knowledge granularity, the determination of ideal decision objective sets and the ranking method, so as to increase the reliability and accuracy of ranking evaluation index.

Satellite X-band SAR data exploitation trends in the framework of ASI’s COSMO-SkyMed Open Call initiative

Abstract COSMO-SkyMed (Constellation of Small satellites for Mediterranean basin Observation) is an Italian Earth Observation (EO) and Dual-Use (Civilian and Defense) Space System conceived with the aim to establish a worldwide service providing Synthetic Aperture Radar (SAR) data, products and services compliant with well-established international standards and relevant to a wide range of applications, such as emergency and risk management, scientific and commercial applications and defense Applications. Starting from 2007, the Italian Space Agency (ASI) has promoted the COSMO-SkyMed SAR data exploitation through several initiatives such as “Announcement of Opportunity”, “Open Call for Science” and “Open Call for National Small and Medium Enterprises (SMEs)”. This paper is focused on the trends and results observed in the context of the “Open Call for Science”, an initiative completely addressed to the national and international scientific community since 2015. In particular, in this framework, the observed trend and advances in science and applications of SAR imagery and Interferometric SAR (InSAR) are summarized and assessed, also in light of the scientific evidence published in the recent literature by scientific users of ASI’s “Open Call for Science”.