Emergency Rescue Operations Research Articles

ОСОБЕННОСТИ ОКАЗАНИЯ ПЕРВОЙ ПОМОЩИ И ПСИХОЛОГИЧЕСКОЙ ПОДДЕРЖКИ ДЕТЯМ В ЭКСТРЕННЫХ И ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

The issue of interaction of employees of EMERCOM of Russia with children is highlighted. The activities and measures contributing to maximizing the effectiveness of emergency rescue, evacuation, and rescue operations involving children are presented. Revealing the issue of national security of the country, we touch upon the description of the protection of the population and territories from emergency situations. Protection is provided by the availability of highly qualified specialists. Special attention is paid to such components as: knowledge of child psychology, physical fitness, personal qualities (responsibility, endurance, sociability, observation), stress resistance. The rationale for the measures of a careful algorithm of work when working with children is as follows: in the event of an emergency, it is necessary to act decisively and quickly, and at the same time carefully and thoughtfully.

Passive unmanned maritime search and rescue routing method

Addressing the challenges inherent in passive unmanned search and rescue missions at sea, including difficulties in target identification, broad search areas, and slow route planning, a strategic process was introduced for maritime search and rescue area planning and a route planning model specifically designed for passive unmanned missions. By thoroughly understanding the emergency response operations at sea and the specific needs for route planning, an optimal routing model have been developed considering factors such as the efficiency of search and rescue area coverage and the cost of rescue routes. The objective function is constructed within these constraints and solved using the Whale Optimization Algorithm. The validity of the model is confirmed through designated scenario experiments, indicating that our proposed model for maritime passive unmanned search and rescue route planning is capable of swiftly identifying the search and rescue area and efficiently discovering a route with reduced costs. This innovation offers valuable decision-making support for the planning of future emergency maritime search and rescue operations in both military and civilian sectors.

Formal Modelling and Model Checking of a Flood Monitoring and Rescue System: A Case Study of Safety-Critical System

The flood incidents are becoming more often and severe, thus extreme events require efficient and effective means of controlling and saving lives and property. The reason for this paper is to use Formal Modelling and Model Checking to analyse a new safety critical Flood Monitoring and Rescue System (FMRS) that shall form the basis for the efficient response to floods. Employing the TLA+ analysis, which outlines the FMRS’s dynamic behavior and operational specifications comprehensively. It is important to stress that in our work we address one of the most exciting directions of applying formal methods for the first time in collaboration with real-world safety-critical system designers and offer a powerful and transparent systematic approach to verifying safety-critical systems’ correctness, safety, and reliability. The TLA+ specifications are very carefully designed to represent multiple aspects of the FMRS, such as sensor systems, communication interfaces, as well as the rescue activity itself. To this end, we use model checking methodologies in order to assess the system’s compliance with the required safety properties, including timely detection of floods, correct delivery of data, and synchronization of rescue operations. The performed model checking demonstrates the presence of essential information about the system’s potential failure and weaknesses, which can be used for FMRS architecture improvement and development. Thus, this case shows that the best use of formal methods exist not as ad hoc methods for resolving some issues in the development of safety-critical systems, but a structured template that could be applied in other domains where high degree of assurance in the reliability of a system is needed. Besides the novel method for the better future of the field of formal verification, the proposal also sketches functional relevance of integrating the effective and efficient approaches for monitoring floods and emergency rescue operations in real-world contexts.

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.

Experimental study on the effect of longitudinal ventilation on spill fire characteristics in sloped tunnels

A series of spill fire tests were carried out in a reduced-scale tunnel (6 m × 0.6 m × 0.45 m), considering different discharge rates (60–140 ml/min), ventilation rates (0∼1.26 m/s), and tunnel slopes (3 %, 5 %, 7 %). Some crucial characteristic parameters during the quasi-steady combustion stage of tunnel spill fires were analyzed, including the diffusion shape of the liquid fuel, mass burning rate per unit area, flame morphology and plume temperature, and the critical ventilation velocity. The results show that as the tunnel slope increases, the diffusion shape of the liquid fuel transitions from circular to an ellipse shape and then to rectangular or linear shape. Moreover, the mass burning rate per unit area decreases with increasing ventilation rates at low ventilation (V < 0.5 m/s), while it remains almost constant at high ventilation rates (V ≥ 0.5 m/s). This could be attributed to variations in flame tilt behavior under different ventilation conditions. Predictive models for the flame tilt angle and the vertical temperature distribution of tunnel spill fires under the effect of ventilation were further established and verified. Furthermore, the critical ventilation velocity of tunnel spill fires remains essentially unchanged as the discharge rate (or fire source power) increases. This phenomenon was explained by analyzing the resistance of the spill fire plume and the buoyancy generated by the smoke temperature. This work could serve as a critical reference for assessing disaster risks and executing emergency rescue operations during tunnel spill fires.

Clustered remote sensing target distribution detection aided by density-based spatial analysis

Small target detection in remote sensing is integral to a range of applications, including smart city systems and emergency rescue operations. However, the challenges posed by weak features and complex backgrounds in remote sensing images have hindered the efficacy of detection. Current models tend to focus on identifying individual targets, resulting in algorithms with larger parameters, slower detection efficiency, and difficulty in striking a balance between false positives and negatives. Given that many tasks do not require precise target location, a more efficient approach involves swiftly predicting target areas with models involving fewer parameters. This paper introduces the concept of group target distribution detection, gathering targets with similar distances and semantic similarities for clustered detection. A Gaussian probability map, formed from target density, is used to train a probability prediction model. We propose a new metric for evaluating this innovative group target distribution detection paradigm and provide a comparative assessment with traditional single-object detection models. In experimental evaluation, our proposed DenseUGE network — employing ResNet34 and ResNet50 as its backbone — surpasses the best baseline method by 3.37% on the AI-TOD dataset using our metrics. Additionally, visualizations demonstrate the ability of our proposed methodology to effectively identify the concentrated distribution of small target groups.

Proposal of a flood damage road detection method based on deep learning and elevation data

Identifying an inundation area after a flood event is essential for planning emergency rescue operations. In this study, we propose a method to automatically determine inundated road segments by floods using image recognition technology, a deep learning model, and elevation data. First, we develop a training model using aerial photographs captured during a flood event. Then, the model is applied to aerial photographs captured during another flood event. The model visualizes the inundation status of roads on a 100-m mesh-by-mesh basis using aerial photographs and integrating the information on whether the mesh includes targeted road segments. Our results showed that the F-score was higher, 89%–91%, when we targeted only road segments with 15 m or less. Moreover, visualizing in GIS facilitated the classification of inundated roads, even within the same 100-m mesh, which is a relevant finding that complements deep learning object detection.

Design and Research of an Elevator Electric Brake Release Device with a Safety Detection Function

Abstract In response to the issues and defects associated with the current method of using electrical devices to open brakes for emergency rescue, a new elevator electric brake release device applicable to any elevator employing an electromechanical braking system has been designed. This device mainly consists of a car data collection unit and a brake release control unit, utilizing the STM32F103C8T6 microcontroller as the control core for elevator emergency rescue operations. The car data collection unit is responsible for gathering and transmitting data on the car’s operational state and position, while the brake release control unit evaluates the conditions for emergency rescue operations and performs real-time analysis, storage, and display of the collected data. It also decides whether to output a brake release signal. Experimental results show that when conditions for rescue operations are met, the electric brake release device can ensure the safe operation of the elevator; when abnormalities are detected, it outputs a signal to stop operations, alongside fault alarms and status displays.

Optimal Allocation Model for Maritime Emergency Resource Considering the Spatial Correlation between Accident Hotspots

For effective maritime traffic emergency rescue (MTER) operations in the event of maritime traffic accidents (MTAs) and to improve rescue efficiency, it is necessary to analyze the MTER synergy problem and the cooperation between port states. First, the spatial information of accidents under the geographic information system data structure is clarified from the global integrated shipping information system of the International Maritime Organization, and the density-based spatial clustering of applications with noise algorithm is used to conduct hotspot mapping analysis of MTAs to establish the clustering and classification of accident characteristics in key areas. Second, the classification characteristics of accident samples are extracted based on spatial information, and the correlation attributes between MTA hotspots are analyzed. Furthermore, by introducing complex network measurement technology, a topological model of the MTER network is established considering the correlation of accident hotspots, and this model is combined with the sample data of MTAs in Southeast Asian waters from 1990 to 2022. Third, the MTER topological network model is quantitatively analyzed under the accident space of Southeast Asia, and the degree of correlation of traffic accidents in key areas is obtained to reveal the inevitable demand for MTER between regions. The results of the analysis show that there is a network correlation between inter-regional accident hotspots, and thus the degree of correlation between accident hotspots needs to be considered for MTER in key areas. Countries in densely connected regions would set up joint rescue exercises and consider rescue assistance between port country stakeholders, thus improving protection for accident emergency responses. The method of complex network topology based on spatial correlation between accident hotspots suggests a new approach for solving the MTER problem.

Returners and explorers dichotomy in the face of natural hazards

Understanding human mobility patterns amid natural hazards is crucial for enhancing urban emergency responses and rescue operations. Existing research on human mobility has delineated two primary types of individuals: returners, who exhibit a tendency to frequent a limited number of locations, and explorers, characterized by a more diverse range of movement across various places. Yet, whether this mobility dichotomy endures in the context of natural hazards remains underexplored. This study addresses this gap by examining anonymized high-resolution mobile phone location data from Lee County, Florida residents, aiming to unravel the dynamics of these distinct mobility groups throughout different phases of Hurricane Ian. The results indicate that returners and explorers maintained their distinct mobility characteristics even during the hurricane, showing increased separability. Before the hurricane, returners favored shorter trips, while explorers embarked on longer journeys, a trend that continued during the hurricane. However, the hurricane heightened people’s inclination to explore, leading to a notable increase in longer-distance travel for both groups, likely influenced by evacuation considerations. Spatially, both groups exhibited an uptick in trips towards the southern regions, away from the hurricane’s path, particularly converging on major destinations such as Miami, Fort Lauderdale, Naples, and West Palm Beach during the hurricane.

Deployment Method for Aircraft-Based Maritime Emergency Communication Resource Reserve Bases

Maritime emergency communication facilities play a crucial role in establishing communication links between land and sea, serving as essential communication means for maintaining maritime safety, disaster response, and emergency rescue operations. With the increasing frequency of marine activities, the rapid response capability of maritime emergency communication is becoming increasingly critical. With their characteristics of high-speed mobility, broad coverage and flexibility, aircraft serve as carriers for emergency communication facilities. The selection of aircraft bases is paramount in meeting the requirement of rapid response for maritime emergency communication. In this paper, we present a multi-objective optimization model for site selection by considering the coverage capabilities of different carriers. The model incorporates hierarchical coverage of unmanned aerial vehicles (UAVs) and helicopters with a genetic algorithm. Through a case study of the Bohai Sea, this paper verifies the feasibility and effectiveness of the model.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

BDHE-Net: A Novel Building Damage Heterogeneity Enhancement Network for Accurate and Efficient Post-Earthquake Assessment Using Aerial and Remote Sensing Data

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

Rapid determination of source parameters of the M6.2 Jishishan earthquake in Gansu Province and its application in emergency response

In this study, we swiftly determined the focal parameters (focal mechanism, seismic imaging process, magnitude) of the Jishishan earthquake, leveraging a solved fault model to assess the intensity field and casualties promptly. The investigation began by retrieving the source mechanism through the P-wave initial motion and W-phase method. This enabled us to chart the spatial and temporal distribution of energy release in the source area via the back-projection technique. Following this, we estimated the earthquake's intensity field by merging the source inversion findings with the ground motion prediction equation. This analysis facilitated the evaluation of earthquake casualties, utilizing the theoretical intensity field and a casualty assessment model. Our findings indicate that the fault type is a thrust fault, characterized by a unilateral rupture in the direction of NW, with a rupture length spanning approximately 10–15 ​km and a duration ranging between 8 and 10 ​s. The earthquake's magnitude varied from M 5.9 to M 6.2. The demarcated high-intensity areas, as per our intensity assessment, align closely with the actual survey results. Furthermore, the predicted total casualties and identified critical rescue zones closely match the real-world casualty figures. These insights offer crucial technical support for governmental emergency command and rescue operations.

A clustering-based analysis method for simulating seismic damage of buildings in large cities

Earthquakes are major threats to cities. Refined simulations of urban earthquake damage are significant for disaster prevention planning, including post-earthquake emergency rescue operations. However, this approach is time-consuming and computationally demanding. This study presents a novel machine learning method for simulating seismic damage of large urban building groups, thus minimizing the computational time and resources associated with the simulation process. First, the proposed method clusters building structures based on their seismic damage index (DI) under specific ground motions. Subsequently, one structure is selected from each cluster for seismic analysis. This approach significantly reduces the number of analyzed structures. The efficiency and accuracy of this method are studied based on a parametric study that involved an improved K-means clustering algorithm and a grid-based clustering algorithm. A batch-clustering algorithm is also developed to further improve the speed of clustering and efficiency of regional seismic simulations. A large Chinese city is considered as research application example. The results indicated the following: (1) the method based on the improved K-means clustering algorithm was superior; (2) the batch-clustering algorithm significantly speeded up clustering analysis and enhanced efficiency of the seismic simulation; (3) the clustering-based simulation method demonstrated high efficiency and accuracy, with a significant decrease of 90.5% in the calculation time compared with direct nonlinear time history analysis. In addition, the average relative error of the DI was only 11.0%. Moreover, over 85.0% of the structures were estimated to be in the correct damage state.

YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI

Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

Impact of risk preferences on evacuee behavior and attention distribution in urban underground space evacuations

Evacuation and conducting emergency rescue operations in underground spaces pose substantial challenges. However, limited work has been done to investigate how evacuees’ heterogeneity influence the evacuation efficiency. In this study, we conducted virtual environment experiment and on-site experiment to analyze the physical behavior and attention distribution of evacuees with different risk preferences during route selections. A total of 30 participants were clustered into three groups: Risk Averter (RA), Risk Neuter (RN) and Risk Seeker (RS), based on the proportion of choosing safe detours. Eye metrics analysis revealed that RA allocated more time to gaze at risk elements, whereas RS tended to pay more attention to the route distance (P =0.003). The total fixation time for RA and RS was about 300 ms longer than RN (P =0.007), indicating that RN exhibited more cautious behavior pattern when selecting an evacuation route. In addition, all participants displayed a noticeable gaze bias towards potential choices during the decision-making process. Specifically, gaze likelihood increased and exceeded 50% at 300–400 ms before decisions were made. Additionally, evacuation models were established based on AnyLogic to investigate the influence of evacuees’ risk preference on evacuation efficiency. The evacuation simulation results demonstrated a significant improvement in evacuation efficiency when all evacuees adopted a risk-averse approach. This resulted in a reduction of evacuation time by 12.07–28.84% compared to the benchmark condition. The findings offer valuable insights into the behavior prediction in the evacuation simulation and emergency management in urban underground spaces.

A Three-Stage Stochastic Model to Improve Resilience with Lateral Transshipment in Multi-Period Emergency Logistics

Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations.

ОРГАНИЗАЦИЯ УПРАВЛЕНИЯ ПОДРАЗДЕЛЕНИЯМИ СЛУЖБЫ ПРОТИВОПОЖАРНОГО И АВАРИЙНО-СПАСАТЕЛЬНОГО ОБЕСПЕЧЕНИЯ ПОЛЕТОВ ДЛЯ ВЫПОЛНЕНИЯ ЗАДАЧ ПО ТУШЕНИЮ ПОЖАРОВ И СПАСЕНИЮ ЛЮДЕЙ СОГЛАСНО ТРЕБОВАНИЯМ ПОЖАРНОЙ ЗАЩИТЫ В ЗАВИСИМОСТИ ОТ ПРИСВОЕННОЙ АЭРОПОРТУ КАТЕГОРИИ

The normative and technical requirements are considered, according to which the assignment of categories to airports according to the unified All-Russian classification – the level of required fire protection, as well as a set of measures through which staffing of fire departments and emergency rescue units with forces and means is organized, as well as, according to these categories, staffing of units with personnel according to the staff of employees fire and rescue flight support services. Both the organization of preliminary planning and the combat management system for a real fire during the conduct of combat operations to extinguish fires, as well as emergency rescue operations in an aircraft accident situation, fully depend on these factors.

Optimization of 3D Coverage Layout for Multi-UAV Collaborative Lighting in Emergency Rescue Operations

Optimization of 3D Coverage Layout for Multi-UAV Collaborative Lighting in Emergency Rescue Operations