Effective Emergency Response Research Articles

Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using Hybrid Metaheuristic Algorithms

In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios. By dynamically adapting to environmental complexities and user mobility patterns, it advances state-of-the-art emergency communication systems, offering a robust framework for real-world applications in disaster resilience and recovery.

Cognitive Modeling for Effective Emergency Response: An Agent-Based Simulation Architecture

Cognitive Modeling for Effective Emergency Response: An Agent-Based Simulation Architecture

Markov-Decision-Process-Based Reinforcement Learning for Emergency Vehicles of Dispatch and Reallocation Strategies through the Lens of Efficiency and Fairness: Evidence from Chinese Emergency Medical Services System

Efficient proactive emergency vehicle planning is crucial for effective emergency response management systems. This paper develops a flexible deep reinforcement learning framework to address uncertainties in dynamic emergency requests and complex traffic conditions. The Markov decision process model is proposed to optimize ambulance dispatch and reallocation, focusing on minimizing average response time, reducing delays from traffic congestion and patient severity misclassification, and enhancing general fairness. To tackle the challenges of the unbounded MDP state space, we employ a least-squares-based approximate policy iteration model for the upper bound and a linear programming model for the lower bound. We use Chinese emergency medical services system data for computational experiments and select average response time, fraction of delay, risk level, and Gini coefficient to evaluate the performance of our model. The numerical results demonstrate our optimal flexible policy outperforms the first-in-first-served rule, improving both efficiency and equity in medical decision-making.

Using Continuous Quality Improvement in Community-based Programming During Disasters: Lessons Learned from the 2015 Ebola Crisis in Sierra Leone.

This paper describes the CQI (Continuous Quality Improvement) process of collecting and analyzing field level qualitative data in an ongoing cycle. This data can be used to guide decision-making for effective emergency response. When medical and community components are integrated from the earliest stages of the disaster, it allows for true collaboration and supports the CQI process to be responsive to evolving data. Our CQI process identified and addressed gaps in communication and coordination, problems with strategy implementation and, on a conceptual level, gaps in the disaster response model. The 2015 Ebola crisis in Sierra Leone provided a case study demonstrating improved effectiveness when a CQI approach is implemented in the Humanitarian Setting, equally in terms of reducing disease spread, and in meeting the broader needs of the population served.

Study on Emergency Decision-Making of Mine External Fires Based on Deduction of Precursory Scenarios

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires.

Experimental Study on Smoke Spreading Laws for Tunnel Fires with Moving Fire Source

Compared with a stationary fire, the spread of smoke in a moving fire source is more complex, which means hazardous consequences. The study of smoke flow and distribution of ceiling temperature is essential for effective emergency response strategies and efficient execution of rescue operations. To study the smoke flow characteristics of a tunnel fire with a moving fire source, we constructed a 1:20 scale tunnel model and conducted experiments with a moving fire source. Six different moving velocities (0.1, 0.15, 0.3, 0.5, 0.8, and 1.0 m/s) were selected for the experiment. The focus of the experiment was to analyze the stability of the smoke and the temperature distribution of the tunnel ceiling under the moving conditions of different fire sources. The results show that when a fire source velocity is below 0.3 m/s, countercurrent smoke occurs in the tunnel. The smoke layer is stable, and the temperature in the center of the tunnel exceeds the sidewall temperatures. When the velocity reaches 0.3 m/s, smoke inversion disappears, the smoke layer begins to become unstable, and the temperature of the tunnel increases while a central zone of low temperature is formed. The faster the fire moves, the longer the central low-temperature zone will be in the tunnel.

Identification and ranking of oil pollution by developing a preventive management and emergency response model against pollution: A case study of a mega port on the northern coast of the Persian Gulf

Oil pollution remains a critical environmental challenge in maritime transportation, significantly impacting marine ecosystems and coastal communities. This study investigates oil pollution incidents at the loading and unloading docks of Imam Khomeini Port and aims to develop a comprehensive prevention and emergency response management model. We conducted a thorough evaluation of environmental components, specifically Total Petroleum Hydrocarbons (TPHs), and utilized the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify and prioritize risks. Key findings revealed four emergency pollution scenarios, highlighting critical areas that require immediate intervention. Our analysis underscored essential agenda items for effective emergency response planning, scoring 4.4 for environmental resource conservation and 4.35 for pre-incident responsibility assignment. Furthermore, a McKinsey Gap Analysis identified seven strategic priorities for the emergency response plan, indicating substantial weaknesses in the strategy (1.65) and skills (1.75) dimensions, demonstrating inadequate crisis preparedness. These results emphasize the urgent need for a robust emergency response model tailored to mitigate oil pollution risks. By addressing identified gaps, this study provides a strategic framework for enhancing environmental management practices at the port, fostering more sustainable operations in the Persian Gulf region. Ultimately, our findings advocate for collaborative efforts among stakeholders, including national and international organizations, to implement rigorous environmental policies and effective monitoring systems to safeguard marine environments against oil pollution.

Nurse Managers' Experiences in Organisational Adaptation During Public Health Emergencies: A Qualitative Study.

To elucidate strategies for improving organisational adaptation during public health emergencies from the perspectives of nurse managers. This study utilised a qualitative approach, incorporating complex adaptive system theory within a phenomenological tradition. Semi-structured interviews were conducted from May 2022 to June 2022. Participants included core members of the Shanghai Public Health Emergency Teams-Nursing from 12 tertiary hospitals in Shanghai, all of whom had substantial experience as nurse leaders impacting health systems. Data obtained were coded and analysed using interpretative phenomenological analysis. Seventeen frontline nurse managers participated, leading to the emergence of three key themes related to complex organisational adaptation: seeking institutional support for environmental adaptation, building intrateam support for structural adaptation and activating individual support for behavioural adaptation. A conceptual framework of organisational adaptation was proposed, displaying the interconnections among these themes. The study provides valuable insights into managing nursing teams involved in public health emergency preparedness and response as an adaptive system. Identified managerial strategies offer guidance to ensure effective organisational assembly and optimal utilisation of the nursing workforce within the healthcare system. Recognising the crucial role of nurses in managerial positions, who leverage leadership, adaptability and decision-making skills to coordinate frontline teams, is vital for effective public health emergency response. Their involvement in administrative roles underscores the importance of nursing perspectives in strategic planning, organisational adaptation and enhancing health system resilience during crises. The organisational adaptation strategies effectively adopted by nurse managers provide a reference for international scholars seeking to enhance the function and performance of nursing staff in healthcare system during public health emergencies. Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist. No Patient or Public Contributions.

REAL-TIME GIS FOR MONITORING AND SECURITY

Applications of geographic information systems in the security domain are diverse and cover various aspects such as crisis prevention and management, public safety, national security, critical infrastructure protection and military applications. Real-time GIS is essential for effective emergency response and management because it provides accurate information on the location and dynamics of events.

A semantic augmented approach to FEMA P-58 based dynamic regional seismic loss estimation application

Regional seismic loss estimation (RSLE) is a crucial process in both immediate post-earthquake emergency response and long-term reconstruction endeavors. Over the years, significant progress has been made in RSLE: sensing approaches such as field investigation and remote sensing offers a comprehensive overview necessary for real-time disaster response, while simulation techniques such as the methodology proposed in Federal Emergency Management Agency (FEMA) P-58 series provide insights into the mechanism of disaster development and its potential long-term impacts on urban assets. Nonetheless, challenges persist in the realm of practical RSLE applications. Firstly, a dynamic understanding of a disaster event and its influence is deemed important for effective emergency responses while challenging to achieve in current approaches. Secondly, stakeholders with varying roles, including administrators, rescue teams and ordinary citizens have distinct information requirements for RSLE. Thirdly, the complexity of seismic loss estimation, involving diverse data sources such as building information models, performance models, fragility data and sensor observations, poses interoperability issue. To tackle these issues, this article introduces a dynamic, multi-granularity, ontological representation scheme tailored for RSLE decision-supporting systems. This scheme operates across various scales, from individual building components to broader regional scales by synergistically employing FEMA P-58 guidelines and Semantic Web technologies. Upon the corresponding semantics, a question-and-answer agent powered by large language model is further developed to facilitate interaction requirements within the RSLE process via FEMA P-58 pipeline. The practical efficacy of this approach is validated through a prototype deployed under a real earthquake event, demonstrating its value in real-world scenarios.

Smart management of emergencies in the agricultural, forestry, and animal production domain: Tackling evolving risks in the climate change era

The agricultural, forestry, and animal production domain (AFA domain) plays an essential role in meeting global needs and supporting livelihoods while facing escalating challenges from climate change-induced impacts and extreme natural events. This perspective advocates for urgent strategies to enhance resilience through effective emergency management and prevention measures tailored to this critical domain. The analysis here exposed, which includes elements of ontology and the conceptual approach of an emergency management system encompassing both restoration and prevention aspects, entails three case studies across the AFA domain. Each case study, described by location, timing, nature, and consequences, critically evaluates the implemented risk prevention measures, details the emergency and recovery actions, and highlights shortcomings in response efforts. The analysis, incorporating a retrospective comparative component based on the proposed conceptual model, highlights the importance of identifying lessons learned and potential future applications. It emphasizes the urgent need for a well-structured emergency management strategy that integrates risk mapping and advanced technology to ensure timely and effective responses. The active engagement of domain professionals (agronomists, foresters, animal production doctors) and scholars of AFA domain sciences, as either farm owners or technical advisors, is crucial to optimize intervention strategies. This engagement is especially important for enhancing resilience during recovery phases, aligning with the best international practices such as making use of local knowledge and citizen engagement strategies. Comprehensive training initiatives, also adopting innovative formats and tools including micro-credentials, e-learning platforms, and the applications of generative Artificial Intelligence for learning assistance, as well as new research insights are strategic for coordinated and effective emergency responses across all stakeholders. Collaboration between the different production systems and areas of expertise, raising awareness of the distinction between Civil Protection and Production Protection and fostering their close interconnection, is essential for effective emergency response and long-term resilience.

Deep Learning-Based Method for Predicting Emergency Resources Demand at Hydrogen Refueling Stations

The transition towards green energy has intensified the focus on hydrogen as a clean energy, with hydrogen refueling stations (HRSs) becoming increasingly prevalent. Despite the potential benefits, the inherent risks associated with high-pressure hydrogen storage pose significant safety challenges. Effective emergency response planning is critical to mitigate the consequences of any potential accidents at HRSs. This study developed a deep learning-based model to predict the demand for emergency resources following an accident at an HRS, thereby enhancing the efficiency of emergency responses. A HRS case library was constructed encompassing a variety of accident scenarios, including characteristics, potential casualties, and corresponding emergency resource demands. By employing deep learning algorithms, correlations between accident consequences and emergency resource demands were established, enabling rapid predictions post-accident. The study evaluated various neural network models and found the Random Forest model to be the most accurate for predicting casualties and resource demands. To facilitate user accessibility, a graphical user interface was developed allowing non-technical emergency responders to input accident parameters and receive immediate predictions on casualties and emergency resource demands. The integration of deep learning with emergency response planning presents a significant advancement in risk mitigation strategies for HRSs.

The European Mobile Laboratory: key insights from a decade of outbreak response

Abstract EMLab has responded to emergencies including the West Africa Ebola virus outbreak (2014-2016), the COVID-19 pandemic, and outbreaks of yellow fever, Marburg virus, and Lassa fever. It has deployed mobile labs and experts to many countries, including Guinea, Liberia, Sierra Leone, Nigeria, Uganda, the Democratic Republic of the Congo, Germany, and Greece. With accumulated experience from each deployment, EMLab’s diagnostic expertise has evolved beyond RT-PCR and serological testing to include basic point-of-care tests, haematology, and genomic surveillance capacities, also focusing on interoperability with other response capacities such as EMTs. Critical to mission success, the organization works closely with the WHO and GOARN, and was certified in 2023 as an official German response capacity within the European Civil Protection Pool. The two-year continuous deployment from 2014-2016 was pivotal in shaping EMLab’s evolution, also leading to significant capacity-building efforts in West Africa. In Guinea, for example, a surveillance network of three diagnostic laboratories has been established and successfully detected cases of viral haemorrhagic fevers, including the re-emergence of Ebola virus and the first reported case of Marburg virus in Guinea in 2021. Ultimately, EMLab’s extensive experience demonstrates that effective public health emergency response requires constant evolution, strategic partnerships, and a commitment to building local capacities, paving the way for a more resilient and prepared global health community.

From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases

The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools.

Study of Flooding Behavior and Discharge from Karot Dam in the Event of a Possible Breach by Using the Hydrodynamic Model

This study utilizes the MIKE 11 hydrodynamic model developed by the Danish Hydraulic Institute to simulate flood behavior downstream of Karot Dam under multi-year in-flow conditions. The key parameters analyzed include breach characteristics, flood duration, water depth, flow velocity, discharge rate, and downstream distance. After dam failure, the peak discharge reaches 33,171 m3/s, exceeding the 10,000-year recurrence peak flow of 32,300 m3/s, with a breach duration of 2 h. The estimated peak discharge after simulation using empirical equations and comparative analyses showed maximum flood discharges of 28,187 m3/s, 28,922 m3/s, and 29,769 m3/s, with breach widths of 181 m, 256 m, and 331 m, respectively. The peak discharge predicted to reach the outlet with travel time ranging from 4 h 25 min to 4 h 40 min. Under multi-year average inflow conditions, Mangla Dam faces no risk of failure, with a maximum outflow of 12,097 m3/s and a spillway capacity of 30,147 m3/s. The model accurately predicted discharge values, with a strong correlation coefficient of R2 = 0.9653, indicating strong agreement between the actual water level data and predicted discharge. These insights are essential for developing effective emergency response strategies to mitigate the risks associated with dam failure.

Risk assessment of gas plume dispersion in bunkering of alternative fuel through validated computational fluid dynamics approach

Abstract Maritime decarbonization is crucial in fighting climate change. Adopting low-carbon alternative fuels could help reduce greenhouse gas (GHG) emissions by 50% by 2050 compared to 2008. While liquified natural gas (LNG) has gained a strong foothold in the maritime sector over the past decades, the trend is shifting toward adopting lower and zero-carbon hydrogen and its carriers, such as methanol and ammonia, as alternative fuels. Quantifying risks from accidental leakages during storage and transfer of these alternative fuels using numerical models is required and commonly adopted in industries. The process leverages multiple models and tools for risk assessments with different accuracy levels and costs ranging from integral models to Lagrangian techniques and Eulerian approaches such as computational fluid dynamics (CFD). Increasingly, CFD approaches are employed for near-field dispersion analysis to assess the risk of accidental leakages. Despite their high computational cost, CFD models offer better accuracy than integral and Lagrangian models. In this work, we developed a CFD-based framework as a quantitative tool for risk assessment purposes. Our framework aims to (1) offer finer gas dispersion resolution with a detailed representation of surrounding structures and geometries, (2) minimize reliance on assumptions and empirical models to capture flow dynamics and gas dispersions accurately, and (3) mitigate conservative estimates and over-design of safety measures. The framework was validated using experimental data from various field tests, including a recent one involving releasing cryogenic liquids into the atmosphere. After successfully validating and comparing with experiments and other available tools, the proposed framework investigated gas plume dispersion from accidental leakages during fuel bunkering operations in ship-to-ship and shore-to-ship scenarios. The data obtained from CFD simulations was subsequently utilized to construct a surrogate model for fast prediction of gas plume behavior under varying environmental conditions. This facilitated the development of an effective emergency response plan.

Lived Experiences of School Heads During Post-Pandemic: Building Resilience and Navigating Schools to Recovery Amidst Disruptive Times

Crisis management practices among school leaders are critical for effective emergency response and resilience. Crisis management studies are essential because unanticipated crises, such as health concerns and calamities, disrupt the learning process. This phenomenological research explores the lived experiences of school heads during the post-pandemic period when they built resilience and navigated their schools back to recovery. Adapting to changing circumstances, finding effective ways to bridge learning gaps and promoting staff and learner well-being are the major challenges facing school heads after pandemic. Real-life situations are further explored through this research to aid in crisis management. This study is crucial because it aids organizations in their effective planning, rapid response, and recovery from unexpected crises. Eight school heads from the Philippines were selected as participants. The researcher utilized a validated questionnaire to gather data on various school management strategies before and after the pandemic. The post-pandemic posed challenges, needs, and opportunities due to protocols, settings, and requirements while incorporating resilience strategies and implementing proactive school recovery approaches. The ANCHOR model was developed as a crisis management framework based on the findings and observations. This process consists of five stages: acclimatization, necessity appraisal, counter-response and handling, opportune resilience strategies application, and recovery and restructuring. It can be applied by organizations to manage crises and recover, emphasizing crisis management skills to improve organizational performance. The model can aid school heads in making informed decisions and navigate institutions to recovery. Finally, this research encapsulates that crisis management skills are essential to organizational performance, particularly in academic institutions.

Supporting Fire Response: Advanced Spatial Data Analytics for Hydrant Access Assessment

ABSTRACTCalifornia faces increasing wildfire frequency and severity, necessitating effective firefighting strategies along with well‐located infrastructure. Hydrants are vital for urban safety and must comply with established standards to adequately protect people and property. One such standard involves hydrant spacing, critical for ensuring effective emergency response in the event of a fire. Traditional geographic information systems techniques are not well suited to fully assess hydrant networks, especially in the context of spacing given complex road network geographies. This paper introduces an innovative approach to assess hydrant spacing. The developed technique is applied in the evaluation of infrastructure in Santa Barbara, California. The results reveal significant code violations along streets throughout the region, suggesting clustered pockets of vulnerability and risk. Potential strategies emerge from this analysis to enhance firefighting preparedness, offering guidance to local agencies regarding hydrant system limitations across this fire‐prone region.

Evaluating the damage of collapsed bridges using remote sensing technologies: Case study: Baltimore’s Francis Scott Key Bridge

<p class="MsoNormal" style="line-height: 120%; layout-grid-mode: char; mso-layout-grid-align: none; margin: 12.0pt 0cm 6.0pt 147.4pt;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 'Times New Roman'; mso-font-kerning: 0pt; mso-fareast-language: JA; mso-no-proof: no;">Bridges are vital for linking communities and facilitating economic activity. However, in the face of disaster, like ship collisions pose a significant threat to bridge infrastructure, causing structural damage and potential safety hazards. Rapid and precise assessment of the damage is aid for effective emergency response and recovery operations. Remote sensing with near real-time satellite imagery provides the disaster scenario. This paper presents a change detection using pre and post-disaster satellite data for Baltimore’s Francis Scott Key Bridge to identify structural damage due to the collision of the ship with support pillars on 26 March 2024. Both optical and microwave satellite data were used from open data sources and analyzed based on geospatial techniques such as change detection and surface profiling. It is estimated that an 1100 m span of bridge was affected due to this collision, which helped to estimate the damage and mobilize the rescue operations. It may need further validation from ground truth information. Hence, the current study emphasizes the potential of remote sensing satellite data to provide near-real-time impact on disaster analysis.</span></p>

A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.