Emergency Management Decision Research Articles

Emergency management decision-making in a changing world: 3 key challenges

Managing emergencies is taxing for individuals due to the stress of making decisions in dangerous, high-stakes and time-constrained environments. These complex, dynamic environments also make it difficult to coordinate as other responders perform different roles that may have conflicting goals. This study explored some of the challenges faced by emergency management decision-makers through a literature review of 70 papers identified from SCOPUS and EBSCO database searches. Three major challenges for emergency management were identified: stress and fatigue, interoperability and ethical decision-making. Each of these challenges is examined to explore their nature and how they are likely to evolve in the future. This paper provides helpful advice on how to mitigate these challenges. We argue that to better meet these challenges, emergency services organisations need to develop and maintain appropriate doctrine and training, develop a supportive organisational culture and effectively learn the lessons of previous critical incidents.

Digital 3D Hologram Generation Using Spatial and Elevation Information

The evolution of cartography poses challenges in representing three-dimensional terrain accurately on traditional two-dimensional maps. Providing an accurate 3D view of the area, coupled with essential geographic information, is vital for rapid and accurate decision-making in emergency management and response. Holography offers a promising solution by providing immersive three-dimensional visualizations. The field of hologram mapping, although novel, is still developing. Given its nascent stage, several limitations are evident. This study addresses one such limitation—inaccuracies in distance measurement—by presenting a hologram map that integrates two-dimensional and three-dimensional information. Accurate distance information on maps is critical for operational success. We aimed to improve hologram maps by integrating contour lines. Our approach allows users to measure distances from near-perpendicular angles while viewing 3D features from other perspectives. We review current advancements in hologram mapping, highlight existing limitations, and introduce our innovative solution designed to enhance both accuracy and usability. Our experiment resulted in a hologram map that accurately depicts a 3D environment, integrates contour lines, and allows for distance and slope angle measurements. The hologram map fills the research gap by providing accurate 3D visualization and distance measurement, signifying a major advancement in hologram mapping.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

Evaluation of debris-flow building damage forecasts

Abstract. Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model’s simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area.

Optimal decision-making of post-disaster emergency material scheduling based on helicopter–truck–drone collaboration

In the last decades, natural disasters, such as earthquakes and landslides, have occurred frequently, seriously threatening the safety of people’s lives and property. How emergency material is scheduled and delivered efficiently to the affected sites after a disaster has become a critical issue in emergency management. Current studies on emergency material scheduling mainly focus on truck or helicopter transport. Inspired by the success of employing drones in commercial logistics, this work investigates the emergency material scheduling issue based on the cooperative transport of drones, helicopters, and trucks. Specifically, this paper considers the limited transport capacity, road conditions in the early stage of the disaster rescue, and affected sites restricted by road conditions that can only be served by helicopters or drones. The studied problem is formulated as a mixed integer programming model, and a two-stage heuristic algorithm is developed to solve the model. For the proposed model, instances of different sizes are generated, and extensive experiments are performed to test the efficiency of the proposed algorithm. The comparison between the solutions obtained by the two-stage algorithm and Gurobi Solver for the small instances validates the effectiveness of the proposed heuristic algorithm. Experimental results for the larger instances show that the proposed two-stage algorithm can effectively solve the problem of emergency material scheduling. Sensitivity analysis of ten typical instances is performed to provide managerial insights. Finally, a case study of the Sichuan earthquake and the visualization of transport routes are presented. The model and solving approach proposed in this work can provide essential decision references for emergency management decision-making.

Financial instruments of crisis reversal in the Russian economy

Aim. To study the economic crises that occurred in the Russian Federation (RF) from 1991 to 2022 from the point of view of analyzing the causes of their occurrence and the factors of reversal, as well as identifying the chances of overcoming the stages of slowdown and decline in economic dynamics, including those associated with digitalization.Objectives. To reveal the causes of economic crises by identifying the risks realized during this or that crisis; to determine the financial instruments of economic crises reversal in the Russian economy since 1991; to identify the chances of the economic system to overcome the stages of slowdown and decline in economic dynamics and to turn the economic system to sustainable growth.Methods. General scientific methods, including comparative, retrospective and analogy methods, were used as methodological tools.Results. The article identifies the realized risks of economic crises in the Russian Federation, from the crisis of 1991 to the crisis of 2022. As chances of economic growth in the period of digital transformation of the economy and the financial sector, the risks of innovative financial technologies, risks of digitalization of the banking sector, risks of insurance and financial services were identified. Financial and non-financial tools to turn the economic crisis to the recovery period and subsequent sustainable growth were identified.Conclusions. Any crisis is based not only on factors that aggravate and destabilize its parameters, but also on factors that present new chances for further economic development. When assessing economic dynamics during crises, one should take into account the realized risks, possible chances and tools for economic turnaround, including financial and non-financial instruments. Financial instruments, which allow to implement extraordinary management decisions, serve as instruments of turnaround. Additional state budget expenditures from the reserve fund (until 2017) or from the national welfare fund represent corrective financing in periods of uncertainty and are associated with emergency management decisions during crises, including during a special military operation. The main task of the regulator is to limit the negative effects of realized internal and external risks.

Authentic spatial vulnerability assessment for evacuation shelters in disaster planning: a case study of Tubay, Agusan Del Norte, Philippines

Authenticity plays a pivotal role in addressing the primary challenges encountered in disaster planning, particularly when it comes to assessing vulnerability and evaluating the spatial distribution of shelter demand and resources. Ensuring the accuracy and reliability of data is essential for effectively managing available resources at each evacuation center, as well as for accommodating evacuees and ensuring the safety of both staff and refugees. In real-life circumstances, the authenticity of the information gathered becomes even more critical, as an imbalance in shelter and population distribution often hinders access to evacuation shelters. In this paper, the researchers introduce a spatial assessment technique that not only analyzes vulnerability but also authentically assesses the spatial distribution of shelter demand and supplies, thereby enhancing the overall effectiveness of disaster planning in Tubay, Agusan del Norte, Philippines. The study focuses on Tubay, Agusan del Norte, Philippines, taking into account spatial accessibility. The researchers found that out of the 33 shelters assessed, 21 are feasible locations for evacuation centers, and approximately 51% of the total population points are vulnerable to flooding. Utilizing GIS maps, the results reveal a significant decrease in overall shelter capacity to 64.26%. The study's findings emphasize the critical need for evaluating the relationship between vulnerable shelters and the demand for resources at each location. This information serves as a practical tool, offering flexibility in terms of data availability for decision-makers, enabling them to investigate areas, and providing a valuable reference for developing and improving emergency management decisions and strategies.

Weakened economic impacts with future intensifying drought in Chinese mainland

Economic loss assessment of drought disaster (ELDA) plays an important supporting role in government emergency management decision-making and the sustainable development of national economy. Previous studies lack projected ELDA for composite drought under the background of global warming, and the evaluation models used have problems such as low calculation efficiency and poor universality. In order to explore the overall economic impact of composite drought disaster in Chinese mainland, an economic loss assessment model of drought disaster (ELDAM) was constructed based on the occurrence process and loss mechanism of drought. Under different global warming levels (GWLs), economic loss of drought disaster (ELD) in Chinese mainland increases with the intensification of drought. When the temperature rise reaches 4 °C, the ELD under the static scenario and the dynamic scenario that considers the future national economic development level can reach 164.00 ± 67.58 and 86.84 ± 38.06 billion USD2015. They only account for 0.014 ± 0.006% and 0.007 ± 0.003% of the projected gross domestic product (GDP), which is much lower than the reference period (2003–2018, 0.12%). The future economic impact of drought in Chinese mainland shows a downward trend against the background of intensifying droughts, but the economic impact of drought cannot be ignored under GWLs. ELDAM can not only be used to estimate ELD from historical drought disasters, but can also be combined with climate predictions to provide advance estimates of ELD, providing a reliable theoretical basis for strengthening regional drought prevention and resistance capabilities.

Acute pancreatitis: Early Management

Abdominal pain caused by acute pancreatitis (AP) is not uncommon in day to day medical practice worldwide. AP is the sudden inflammation of the pancreas, and it may be confined to the pancreas, or may be more life-threatening, affecting all organs and systems. It progresses mildly in 80% of patients and resolves with treatment, but in cases of severe AP, with mortality of around 30% has been recorded. AP is most often established by clinical symptoms and at least threefold raised enzymes and by imaging (any two of these three).In the management AP, treatment is mainly related to the severity of the disease and approaches are constantly being updated. With early diagnosis and treatment, most of the patients can be discharged, and the development of complications and mortality can be reduced. Hemodynamic status should be assessed immediately upon presentation and resuscitative measures begun as needed. Early targeted fluid therapy within the first 48 h is critical to improve the outcome of severe AP. Patients with organ failure and or SIRS should be admitted to an ICU /HDU whenever possible. Emergency management decision are early fluid management strategy, utility and timing of antibiotics, the timing and type of nutritional support and in relevant cases endoscopic retrograde cholangiopancreatography (ERCP) and cholecystectomy approachescan change the course of the disease and the length of stay in the hospital. Therefore, emergency management is important.Enteral nutrition is recommended to prevent infectious complications, whereas parenteral nutrition should be avoided.Routine use of prophylactic antibiotics in patients with severe AP and or sterile necrosis is not recommended.In patients with infected necrosis, antibiotics known to penetrate pancreatic necrosis may be useful in delaying intervention, thus decreasing morbidity and mortality. Successful clinical management requires close interdisciplinary cooperation and coordination from experienced gastroenterologists, intensive care physicians, surgeons, and radiologists. Bangladesh J Medicine 2023; Vol. 34, No. 2(1) Supplement: 188-189

An improved social force model (ISFM)-based crowd evacuation simulation method in virtual reality with a subway fire as a case study

ABSTRACT Crowd evacuation simulation using virtual reality (VR) is significant for digital emergency response construction. However, existing evacuation simulation studies suffer from poor adaptation to complex environments, inefficient evacuations, and poor simulation effects and do not fully consider the impacts of specific disaster environments on crowd evacuation. To more realistically express the crowd evacuation results obtained under the influence of fire environments and the subjective consciousness of pedestrians in subway stations, we designed a dynamic pedestrian evacuation path planning method under multiple constraints, analysed the influences of an ‘environmental role’ and a ‘subjective initiative’ on crowd evacuation, established an improved social force model (ISFM)-based crowd evacuation simulation method in VR, developed a prototype system and conducted experimental analyses. The experimental results show that the crowd evacuation time of the ISFM is affected by the disaster severity. In simulation experiments without disaster scenarios, the improved model's crowd evacuation efficiency improved by averages of 12.53% and 15.37% over the commercial Pathfinder software and the original social force model, respectively. The method described herein can effectively support real-time VR crowd evacuation simulation under multiexit and multifloor conditions and can provide technical support for emergency evacuation learning and management decision analyses involving subway fires.

Large-scale emergency medical services scheduling during the outbreak of epidemics.

This paper studies a new large-scale emergency medical services scheduling (EMSS) problem during the outbreak of epidemics like COVID-19, which aims to determine an optimal scheduling scheme of emergency medical services to minimize the completion time of nucleic acid testing to achieve rapid epidemic interruption. We first analyze the impact of the epidemic spread and assign different priorities to different emergency medical services demand points according to the degree of urgency. Then, we formulate the EMSS as a mixed-integer linear program (MILP) model and analyze its complexity. Given the NP-hardness of the problem, we develop two fast and effective improved discrete artificial bee colony algorithms (IDABC) based on problem properties. Experimental results for a real case and practical-sized instances with up to 100 demand points demonstrate that the IDABC significantly outperforms MILP solver CPLEX and two state-of-the-art metaheuristic algorithms in both solution quality and computational efficiency. In addition, we also propose some managerial implications to support emergency management decision-making.

A review of forest visualization and forest fire simulation technology research

A review of forest visualization and forest fire simulation technology research

Analyzing Emergency News Comments to Assess Online Public Opinions: A Case Study of COVID-19

For the purpose of enhancing the online public opinion intervention mechanism, fostering a positive public opinion environment, it is crucial to examine the rules and traits of online public opinion dissemination from multiple perspectives. The paper proposes a fresh approach to measuring network public opinion by analyzing emergency news comments. In order to develop risk assessment indicators, we first employ the multi-round Delphi method. Then, we organize the "COVID-19" news comments using natural language processing and text clustering techniques, correlating the risk assessment indicators with the risk evolution of emergency events. Finally, we analyze the time evolution trend of users’ participation in network public opinion. Results show that the use of news commentary can effectively predict the tendency of social risk. Therefore, the risk assessment method in this paper can judge and warn the network public opinion in time, which is of great value in assisting major emergency management decisions.

The Benefit of Informed Risk-Based Management of Civil Infrastructures

One of the most interesting applications of Structural Health Monitoring (SHM) is the possibility of providing real-time information on the conditions of civil infrastructures during and following disastrous events, thus supporting decision-makers in prompt emergency operations. The Bayesian decision theory provides a rigorous framework to quantify the benefit of SHM through the Value of Information (VoI) accounting for different sources of uncertainties. This decision theory is based on utility considerations, or, in other words, it is based on risk. Instead, decision-making in emergency management is often based on engineering judgment and heuristic approaches. The goal of this paper is to investigate the impact of different decision scenarios on the VoI. To this aim, a general framework to quantify the benefit of SHM information in emergency management is applied to different decision scenarios concerning bridges under scour and seismic hazards. Results indicate that the considered decision scenario might tremendously affect the results of a VoI analysis. Specifically, the benefit of SHM information could be underestimated when considering non-realistic scenarios, e.g., those based on risk-based decision-making, which are not adopted in practice. Besides, SHM information is particularly valuable when it prevents the selection of suboptimal emergency management actions.

Emergency management decision of urban rainstorm and flood disasters based on similar cases analysis

Under the background of global climate change and rapid development of urbanization, urban extreme occurs more frequently. In consideration of the existing problems in the emergency management decision-making of urban rainstorm and flood disasters in China, this study put forward the decision-making method of urban rainstorm and flood disaster emergency management based on similar cases analysis method. Based on the evolution process of urban waterlogging disaster, the problem attribute system of urban rainstorm and flood disasters was established. The case-based reasoning (CBR) method was used to calculate the global similarity to determine the best historical case. The case-based decision theory (CBDT) method was used to calculate the comprehensive utility value of alternative cases to determine the optimal alternative cases. The empirical analysis was took in Xi'an city, one of the national center cities of China. Results show that the CBR method was used to obtain the highest similarity of historical case P3, which is 0.481. The CBDT method was used to calculate the high similarity of historical cases P3 and P4 to form the similar case set, with the similarity of 0.820 and 0.851, respectively. Combined with the event development, the comprehensive utility values were calculated. When the decision-maker pays the same attention to the emergency effect and response cost, the comprehensive utility value of P3 and P4 is 0.922 and 0.900, respectively, and P3 is the best reference scheme. By comparison, the latter is more suitable. The results can provide scientific basis for emergency management of urban rainstorm and flood disasters.

Ensemble forecast for storm tide and resurgence from Tropical Cyclone Isaias

Ensemble-based probabilistic forecasting of storm surge is increasingly being used to provide metrics for emergency management decisions such as the near-worst case scenario. The Stevens Flood Advisory System is an ensemble prediction system used to forecast total water levels over a broad coastal region and street-scale flood levels for several New York Harbor (NYH) critical infrastructure sites. As a part of our continuous assessment of this system’s performance, we evaluate its prediction of storm tide and resurgence during Tropical Cyclone Isaias (2020), which tracked northward along the Pennsylvania/New Jersey border and caused the largest storm surge in NYH since Hurricane Sandy. Isaias specific track and speed generated an unusual flood event consisting of a storm surge, a blowout, then a significant resurgence that caused minor flooding. The analysis shows that the super-ensemble spread provided an equal or better estimate of uncertainties than sub-ensembles based only on any single meteorological forcing system. Because of ensemble averaging, the central forecast under-predicted peak water levels and the resurgence peak though these were predicted by some of the ensemble members. The impacts of errors in forecast storm arrival time and resolution-related biases in coarse global atmospheric models on the predictions are noted. A limited comparison for this single storm with the National Hurricane Center’s forecast show SFAS providing better accuracy and spread. Advantages and challenges of SFAS and other similar mid-latitude flood forecast systems are identified along with recommendations for analysis and improvement.

Informational Determinants of Large-Area Hurricane Evacuations

Abstract This study reports on two experiments to investigate the informational determinants of hurricane evacuation decisions (temporal and spatial). Whereas most observational and experimental studies in this domain address the public’s response to forecast information, this study addresses emergency management decisions. Using a subject sample of emergency managers and other public safety leaders, contrasted with a more typical university subject pool, this study presents an experimental design that overcomes the counterfactual problem present in all prior published experiments, by relying on an actual storm (Hurricane Rita) with a known outcome. Several methodological advancements are presented, including the use of an established numeracy instrument, integration of advanced hydrodynamic forecasts, and use of a loss aversion frame to improve generalizability. Results indicate that the availability of additional forecast information (e.g., wind speed, forecast tracks) significantly increases the probability and improves the timing of early voluntary evacuation. However, we observe that more numerate subjects are less likely to avoid relying upon forecast information that is characterized by probability (e.g., the uncertainty in the forecast track, sometimes referred to as the “cone of uncertainty”). Consequently, more numerate emergency managers are almost twice as likely as less numerate ones to provide additional evacuation time to their coastal communities, and they do so by longer than a typical workday (8.8 h). Results also indicate that subjects knowingly overevacuate large populations when making spatial mandatory evacuation orders. However, results indicate that numeracy mitigates this effect by more than half in terms of the population subject to mandatory evacuation.

Retracted] Emergency Project Management Decision Support Algorithm for Network Public Opinion Emergencies Based on Time Series

The present study aims at proposing a time series‐based network public opinion emergency (NPOE) management decision support algorithm for the problems of low decision accuracy and long decision time in traditional similar algorithms. In this proposed algorithm, after the time series data are preprocessed, the association rules of the original indicator data of network public opinion emergencies (NPOEs) are mined, the original indicator data matrix of NPOEs will be constructed, and the improved local linear embedding approach will be employed to obtain the original indicator data of NPOEs. After carrying out the preprocessing according to the preprocessing results, the objective weight of the emergency decision index is calculated through the interval value fuzzy information entropy measurement of the emergency decision index, the emergency management decision support model is constructed, and the emergency management decision support of the NPOE is realized. The simulation experiment results show that the proposed algorithm has a better effect on the decision‐making effect of the management of NPOEs, and a higher decision‐making efficiency is achieved.

AHP Based Grassroots Emergency Response Capability Evaluation Index System Construction

The primary and secondary catastrophes of emergencies, as well as their comprehensive disasters, add a significant deal of complexity and risk to the grassroots emergency response system’s creation. The completeness of the emergency response system development is significantly related to improving the basic capacity of grassroots emergency security. The weight comparison calculation and analysis of the links at all levels in the emergency response system is carried out using the hierarchical analysis method based on the basic experience of current domestic related fields for the construction of the emergency response process system, and the results of the relative importance ranking of indicators are obtained, and this is used to provide emergency response and emergency management decision makers with emergent information.