Rescue Forces Research Articles

Application of quality indicators and critical lessons learned assessment as a research approach for the evaluation of rescue missions during terrorist attacks

In Wuerzburg, Germany, a terrorist attack and a killing rampage occurred five years apart (2016 and 2021). Following a structured evaluation of the rescue mission in 2016, a bundle of quality indicators and ten “lessons learned” were defined. Aim of the presented study was to compare the two rescue missions and to critically review the lessons learned from 2016 for their implementation and feasibility. An interdisciplinary and inter-professional group of experts analyzed the data using predefined quality indicators. All lessons defined in 2016 were critically reviewed and qualified as either lessons learned or lessons identified. While seven out of ten lessons were successfully implemented after 2016 (lessons learned), three lessons didn´t work and were recategorized as lessons identified (communication, zoning and the mutual exchange of different tactical approaches). Our results demonstrate that the conclusions drawn in 2016 have helped to improve the performance of the rescue forces in 2021. In addition, the identified lessons are now the basis for further improving emergency and disaster preparedness. It is important to understand, that the process of preparedness improvement is not completed with the definition of lessons identified. These must first be integrated into response plans and then trained intensively. A lesson identified only becomes a lesson learned once it has been successfully applied.

Effect of Motivation on Employee Performance: A Case of Tanzania Fire and Rescue Force, Kigoma Region

This study sought to determine the effect of motivation on employee performance at the Tanzania Fire Rescue Force. The study was specifically designed to identify the effects of salary, fringe benefits, recognition, training & development on the employees' performance using a case of the Tanzania fire rescue force in Kigoma Region. The study employed a quantitative approach and cross-sectional research design. 50 structured questionnaires were distributed to 50 employees and the collected data were analyzed using descriptive statistics, principal component factors, correlation and simple linear regression analysis. The findings indicated the moderate effect of salary, Recognition & Appreciation on employee performance. The findings also revealed that personal commitments and job satisfaction are two fringe benefit factors influencing employee performance. Moreover, it was reported that Training & Development have contributed to 63% of the variation in employees' performance among workers. Additionally, the overall finding indicated that salary, fringe benefits, training, development, Recognition & Appreciation are seen as job-motivating factors that significantly impact employee performance. The study recommended that by nurturing a motivational work environment and supporting skill development and feedback mechanisms, the organization can further enhance employee performance and contribute to its overall success.

Text-Guided Multi-Class Multi-Object Tracking for Fine-Grained Maritime Rescue

The rapid development of remote sensing technology has provided new sources of data for marine rescue and has made it possible to find and track survivors. Due to the requirement of tracking multiple survivors at the same time, multi-object tracking (MOT) has become the key subtask of marine rescue. However, there exists a significant gap between fine-grained objects in realistic marine rescue remote sensing data and the fine-grained object tracking capability of existing MOT technologies, which mainly focuses on coarse-grained object scenarios and fails to track fine-grained instances. Such a gap limits the practical application of MOT in realistic marine rescue remote sensing data, especially when rescue forces are limited. Given the promising fine-grained classification performance of recent text-guided methods, we delve into leveraging labels and attributes to narrow the gap between MOT and fine-grained maritime rescue. We propose a text-guided multi-class multi-object tracking (TG-MCMOT) method. To handle the problem raised by fine-grained classes, we design a multi-modal encoder by aligning external textual information with visual inputs. We use decoding information at different levels, simultaneously predicting the category, location, and identity embedding features of objects. Meanwhile, to improve the performance of small object detection, we also develop a data augmentation pipeline to generate pseudo-near-infrared images based on RGB images. Extensive experiments demonstrate that our TG-MCMOT not only performs well on typical metrics in the maritime rescue task (SeaDronesSee dataset), but it also effectively tracks open-set categories on the BURST dataset. Specifically, on the SeaDronesSee dataset, the Higher Order Tracking Accuracy (HOTA) reached a score of 58.8, and on the BURST test dataset, the HOTA score for the unknown class improved by 16.07 points.

Research on rescue priority based on high spatiotemporal resolution mobile positioning data

Estimate the key rescue areas of earthquake accurately, which is of great significance for deploying rescue forces and implementing rescue activities in post-earthquake scientifically.This paper based on the idea of first zoning, then classification, and then prioritizing rescue, taking the core area of Tangshan City as the study area, based on urban road data and mobile positioning data, combined with GIS methods to achieve street level rescue zoning, k-means clustering analysis is used to classify rescue sectors, and personnel burial model is used to conduct rescue priority classification.The results indicate that rescue priority is closely related to the time of earthquake occurrence. When the earthquake occurs between 18pm and 7pm in the next day, the number of priority rescue sector at level I and II is the highest. When the earthquake occurs between 8am and 11am on weekends, the number of priority rescue sector in residential areas increases, while the number of priority rescue zone decreases in workspace areas. This study provides refined rescue zoning and priority grading in the early stages of disaster relief with the absence of disaster information, which will help to assist in decision-making for professional force dispatch.

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

<div>Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.</div>

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

The Systematic Construction of Emergency Medical Security System in Long-Distance Walking Movement on Urban Paved Roads

Objective To explore the scope of building emergency medical security mechanism in urban paved road environment, and to provide reference, sample data and improvement suggestions for building emergency medical security mechanism in such specific environment. Methods The data of rescue personnel deployment, rescue materials allocation, rescue force monitoring, actual rescue situation, emergency plan and relevant regulations setting, training and other aspects of the emergency medical security mechanism in March and April 2023 were collected, and the data were analyzed. Results (1) The hiking distance was 40km and the road conditions were urban paved roads. There were 1150 official trekkers and 65 people were treated medically. (2) The most common symptoms of outdoor hiking included skeletal muscle injury (19.48%), foot skin and soft tissue injury (18.17%), fever (2.61%) and diarrhea (0.61%). (3) In March, 18.36% of the core medical team members and 20.41% of the first aid volunteers who underwent systematic training took up the task of supporting hiking activities. In April, the core medical team personnel who undertook the task of supporting hiking activities accounted for 1.23% of the total number of the security team, and the first aid volunteers who underwent systematic training accounted for 37.04%. (4) According to local conditions, the multi-vehicle rescue and transport mode was adopted, which was combined with the modified commercial rescue vehicle (42.86%) with the on-board emergency medical unit (EMU) and the ordinary ambulance (28.57%). (5) More than 700 pieces of 34 kinds of medical materials and 40 sets of mobile communication equipment, which were put into use after the feasibility study of the expert group and the technical group, guaranteed the competition from the hardware level. Conclusion The construction of emergency medical support mechanism in outdoor walking sports needs to pay attention to environmental characteristics, operation mode of support team, personnel and adaptability training, equipment and deployment of medical support materials, deployment and cooperation of communication and transport capacity, reasonable support plan and other factors. Medical volunteers who have been systematically trained can undertake part of outdoor medical rescue work. Pre-activity training and regular re-training of medical volunteers can ensure the safety of outdoor activities.

IoT Framework for Early Fire Detection and Control in Public Large Buildings: A Review

Fire destruction in large public and private buildings, particularly in markets, public offices, education, and industries, has devastated properties, costing millions while taking people’s lives. Fire destruction also extends to environmental degradation. Complete fire prevention is unattainable. However, an effort could be made to early fire detection and control to lessen the tragedy. There are several technologies for real-time fire detection, like heat detection, gas detection, flame detection, and smoke detection. A maximal mix of fire-detecting algorithms should be utilized to maximize accuracy and eliminate false fire confirmation. This research paper presents an early fire detection and control framework for large public and private buildings that utilizes the available fire detection technologies reviewed from various pieces of literature. In justification of the framework adaptability, the research paper examined the current state of firefighting infrastructures in large and private buildings. The review was based on ten (10) large public and private buildings. The buildings were divided into four strata (business center's, public offices, education and industries). In addition, information from the Fire and Rescue Force (Mbeya Office) was used to further envision the possibility of the framework adaptation. Generally, effort should be made to ensure old and new large public and private buildings have well-functioning firefighting infrastructures as stipulated in policies and laws for public safety. Furthermore, slight infrastructure improvement is required, particularly in sustainable power supply, internet connectivity, and automatic fire detection and alarm systems, to adapt the presented early fire detection and control framework smoothly.

A Deep Learning Application for Building Damage Assessment Using Ultra-High-Resolution Remote Sensing Imagery in Turkey Earthquake

Rapid building damage assessment following an earthquake is important for humanitarian relief and disaster emergency responses. In February 2023, two magnitude-7.8 earthquakes struck Turkey in quick succession, impacting over 30 major cities across nearly 300 km. A quick and comprehensive understanding of the distribution of building damage is essential for efficiently deploying rescue forces during critical rescue periods. This article presents the training of a two-stage convolutional neural network called BDANet that integrated image features captured before and after the disaster to evaluate the extent of building damage in Islahiye. Based on high-resolution remote sensing data from WorldView2, BDANet used pre-disaster imagery to extract building outlines; the image features before and after the disaster were then combined to conduct building damage assessment. We optimized these results to improve the accuracy of building edges and analyzed the damage to each building, and used population distribution information to estimate the population count and urgency of rescue at different disaster levels. The results indicate that the building area in the Islahiye region was 156.92 ha, with an affected area of 26.60 ha. Severely damaged buildings accounted for 15.67% of the total building area in the affected areas. WorldPop population distribution data indicated approximately 253, 297, and 1,246 people in the collapsed, severely damaged, and lightly damaged areas, respectively. Accuracy verification showed that the BDANet model exhibited good performance in handling high-resolution images and can be used to directly assess building damage and provide rapid information for rescue operations in future disasters using model weights.

Using infrared cameras in drones to detect bleeding events

BackgroundHemorrhage is one of the main causes of death in trauma. Critical bleeding in patients needs to be detected as soon as possible to save the patient. Drones are gaining increasing importance in emergency services and can support rescue forces in accident scenarios such as a mass casualty incident.MethodsIn this study, a simulated pelvic hemorrhage was detected using a drone from 7 m above the ground over a time span of 30 s.ResultsThe results allow a good detection of the pelvic hemorrhage. Nevertheless, the simulated blood cools down quickly. After 30 s, there was no significant temperature difference compared to the rest of the body. At this point, further assessment is only possible via the RGB image.ConclusionThe findings suggest that bleeding from an open and continuously bleeding wound would most likely be detectable using the drone’s thermal imaging camera, even over a longer period of time.

An unmanned device designed for drowning rescue

Prevention and rescue work is considered an urgent and crucial job to ensure the safety and life of people. However, this job contains many unpredictable dangers and difficulties that rescuers encounter during the rescue process due to the lack of timely support equipment. Causing a threat to the safety and life of the victim as well as the rescue force. In this paper, we propose to design a drone to assist in rescue and help people in distress get out of drowning more quickly to increase the chance of survival for people in distress by using unmanned aerial vehicles (drones) to carry a self-pumping buoy. When detecting people drowning, the rescuer can control the drone carrying a buoy to automatically pump out the drowning person's position and release the buoy to the person in distress. Experimental results in a controlled environment show the feasibility of the device in service of rescue work. The device has been tested in specific areas such as football fields, lakes, and open spaces as well as areas with obstacles, and achieved the required results.

Simulation of scenario characteristics of LPG tank explosion accident and its contribution to emergency planning: A case study

Leakage and explosion of hazardous chemicals during road transportation can cause serious building damage and casualties, and adoption of highly-efficient emergency rescue measures plays a critical role in reducing accidental hazards. Considering a liquefied petroleum gas (LPG) transport tanker explosion accident that occurred in Wenling, Zhejiang Province, China on June 13, 2020 as example, this study proposes a risk assessment framework. This framework recreates the leakage and explosion of the accident process using FLACS v10.9, suggests plans for evacuation, describes the rescue areas of different levels, and explores the influence of environmental factors on the evacuation and rescue areas. The results show that simulated and predicted distributions of fuel vapour cloud concentration and explosion overpressure can provide a reference basis for rapid rescue activities; the characterization of the dynamic effects of wind speed, wind direction, and temperature with respect to the evacuation and rescue areas can be used as theoretical support for on-site adjustment of rescue forces. The role of obstacles can prevent the expansion of the evacuation areas under low wind-speed conditions, and the presence of highly congested obstacles determines the level of the rescue area. The results obtained are important for the risk analysis and the development of emergency rescue measures in case of explosion accidents associated with transportation of hazardous chemicals on high-hazard and high-sensitive road sections.

Cross-Departmental Collaboration Approach for Earthquake Emergency Response Based on Synchronous Intersection between Traditional and Logical Petri Nets

In order to reduce the harm of earthquakes to human society, all governments actively promote the construction and development of earthquake emergency rescue work. The earthquake emergency response involves many departments, multiple personnel and large rescue forces, which presents a great challenge to the ability to carry out cross-departmental rescue work in a collaborative and joint manner. A novel collaboration approach based on traditional and logical Petri nets is proposed to improve the cross-departmental collaboration in earthquake emergency response. The approach extends the synchronization of transitions in traditional Petri nets to that of traditional and logical transitions in traditional and logical Petri nets, and defines the intersection of related logical functions. The approach builds a model for the earthquake emergency response plans of various departments with the help of traditional and logical Petri net models, and then performs synchronous intersection operations on the two kinds of Petri nets and merges the two kinds of Petri nets into new logical Petri nets. Meanwhile, through realizing the collaboration ability of cross-departmental work, the approach improves the rescue efficiency and reduces the damage of the earthquake emergency.

Evaluation of the decision-making process within the table-top exercise of the Terror and Disaster Surgical Care (TDSC®) course

PurposeThe threat of terror is omnipresent in Europe and the number of attacks worldwide is increasing. The target of attacks in Europe is usually the civilian population. Incalculable dangerous situations at the scene of the event and severe injury patterns such as complex gunshot and explosion injuries with a high number of highly life-threatening people present rescue forces, emergency physicians and subsequently hospitals with medical, organizational as well as tactical and strategic challenges. The Terror and Disaster Surgical Care (TDSC®) course trains clinical decision-makers to meet these challenges of a TerrorMASCAL in the first 24–48 h.MethodsA table-top exercise was developed for the TDSC® course as a decision training tool, which was prospectively evaluated in six courses. The evaluation took place in 3 courses of the version 1.0, in 3 courses in the further developed version 2.0 to different target values like, e.g., the accuracy of the in-hospital triage. Furthermore, 16 TDSC® course instructors were evaluated.ResultsFor the evaluation, n = 360 patient charts for version 1.0 and n = 369 for version 2.0 could be evaluated. Overall, the table-top exercise was found to be suitable for training of internal clinical decision makers. Version 2.0 was also able to depict the action and decision-making paths in a stable and valid manner compared to the previous version 1.0. The evaluation of the instructors also confirmed the further value and improvement of version 2.0.ConclusionWith this prospective study, the table-top exercise of the TDSC® course was tested for decision stability and consistency of the participants’ decision paths. This could be proven for the selected target variables, it further showed an improvement of the training situation. A further development of the table-top exercise, in particular also using digital modules, will allow a further optimization.http://www.bundeswehrkrankenhaus-ulm.de

Rethinking the Current "Stage-and-Wait" Paradigm.

The experience of terrorist incidents involving a secondary explosive device that targeted rescue forces led to changes in the safety protocols of these forces in most countries of the world. These protocols are the foundation of the current "Stage-and-Wait" paradigm that prohibits the entry of Emergency Medical Services (EMS) from entering the scene and treating casualties until it is deemed safe. These guidelines were established absent of an evidence-base detailing the risk to responders and the potential consequences to the injured on-scene. The lack of clarity is compounded by the fact that different situations, as well as operational considerations, such as the length of time until bomb squad arrival at the scene versus time of massive bleeding injuries, for example, impact outcomes must be taken into account. This study sought to shed light on this matter while employing an evidence-based approach exploring the investigations of the frequency of secondary explosion threats in terrorist attacks over the last 20 years and discussing some of the ethical challenges and ramifications ensuing. While this study does not propose an outright change to current guidelines, in light of the evidence gathered, an open review and discussion based on the findings may be beneficial. The Global Terrorism Database (GTD) was used as the data source of bombing incidents world-wide. The results revealed that approximately 70 per-1,000 bombing incidents involved secondary explosions across regions and countries within the study period. This study emphasizes the need to rethink the current "Stage-and-Wait" paradigm by recommending brainstorming conferences comprised of multi-sectoral experts aimed at deliberating the matter. World-wide experts in emergency medicine, bioethics, and disaster management should cautiously consider all aspects of bomb-related incidents. These brainstorming deliberations should consider the calculated risk of secondary explosions that account for approximately 70 per-1,000 bombing incidents. This study highlights the need to re-examine the current versus new paradigm to achieve a better balance between the need to ensure EMS safety while also providing the necessary and immediate care to improve casualty survival. This ethical dilemma of postponing urgent care needs to be confronted.

Spatial prediction of the geological hazard vulnerability of mountain road network using machine learning algorithms

The current assessment index of the geological hazard vulnerability assessment for mountain road network is relatively simple, and the assessment methods used are subjective, complex, and inefficient. This study proposes a prediction model for geological hazard vulnerability assessment of mountain road network incorporating machine learning algorithms. First, based on the quantification of the characteristics of the mountain road network and the local rescue forces, an objective and reasonable index-based system of vulnerability assessment of the mountain road network was constructed by combining the population, economic, and material factors. Second, the FAHP and AHP-TOPSIS were applied for the development of the vulnerability assessment models to carry out the preliminary vulnerability assessment for different road types. Third, the results of the preliminary vulnerability assessment were used as the sample set to build a road vulnerability prediction model using SVM, RF, and BPNN algorithms. Finally, the five-fold cross-validation and statistical parameter accuracy analysis were conducted to determine the most reasonable model with the highest prediction accuracy for geological hazard vulnerability mapping of the mountain road network. The results indicated that the vulnerability prediction model based on the FAHP sample set using the RF algorithm demonstrated the highest accuracy and robustness.

Prediction and Analysis of Nuclear Explosion Radioactive Pollutant Diffusion Model

This study presents a model for the dispersion of radioactive smoke clouds from a nuclear weapon explosion. A model based on a modified Settlement model is chosen to simulate the dispersion of radioactive contaminants from a nuclear explosion in the atmosphere. The arrival time and dose rate of radioactive fallout at various distances in the downwind direction are given for different equivalents of the surface explosion and typical meteorological conditions. Thus, the prediction of the dispersion of radioactive contaminants from a nuclear explosion can be achieved under the conditions of known nuclear explosion equivalence and local meteorological parameters. This provides a theoretical basis for the estimation of the affected environment and the input of rescue forces after the explosion.

A model-based early warning system for runoff-generated debris-flow occurrence: Preliminary results

Early warning systems for debris flows are low cost measures for mitigating this kind of hazard. The early warning systems provide a timely alert for upcoming events in order to take protective measures, such as closing railways-roads, evacuating people from the threatened areas, and put rescue forces into readiness. These systems usually are sensor-based, and the alert time is the interval between the timing of the first detachment of debris flow by a sensor and its arrival into the threatened area. At the purpose of increasing the alert time, we propose an early warning system based on a model-cascade: nowcasting, hydrological- and triggering models. Nowcasting anticipates rainfall pattern that is transformed into runoff by the hydrological model. The triggering model estimates the volume of sediments that the runoff can entrain, and compares it with a critical threshold. If this is exceeded the alert is launched. The proposed early warning system is tested against the available data of the Rovina di Cancia (Northeast Italy) site.

A discrete teaching–learning based optimization algorithm with local search for rescue task allocation and scheduling

The allocation and scheduling of the emergency rescue forces is a fundamental task in emergency management. This paper aims to address the allocation and scheduling problem to minimize the average completion time of all rescue teams by using a discrete teaching–learning based optimization algorithm with local search (DTOLS). First, an improved k-means clustering algorithm with constraints is proposed to assign tasks to rescue teams based on the location of rescue tasks. Second, a hybrid discrete optimization algorithm based on a teaching–learning mechanism is designed to generate the task scheduling sequence for each rescue team as an initial solution. Next, an efficient two-phase local search strategy is presented to improve the current solution. For three neighborhood task moves based on problem characteristics, which contains insert task within a team, swap tasks within a team, insert task between teams, the speed-up techniques are introduced to reduce the computational complexity of calculating completion time of a rescue team. Finally, the parameters of DTOLS are calibrated by Taguchi method to determine appropriate values. DTOLS is compared with the state-of-the-art algorithms, and the experimental results demonstrate the effectiveness of DTOLS in solving a set of test instances.

Problems and Directions of Cooperation of the National Rescue and Firefighting System with the Structures of Local Self-Government

Cel: The aim of the article is to identify directions for improving cooperation between the national rescue and firefighting system (KSRG) and local government units. The authors of this article have analysed the structure of KSRG and the powers of local government in the area of public safety, with particular emphasis on issues relating to the principles of operation, potential and tasks of the rescue and firefighting system, as well as the organizational structure of local government and its tasks in maintaining public safety. The discussion of the cooperative strategy of KSRG and local government units was also a contribution to formulating conclusions and postulates that can be used to set new directions for improving cooperation between the two entities. This is important due to the fact that they play an important role in building a security system for citizens and their property. Wprowadzenie: Ensuring security is one of the top priorities in every country’s policy. In the area of public safety, in addition to national units (State Fire Service, State Medical Rescue) and local government units, properly prepared, organized and specialized formations such as OSP, ZHP, WOPR, GOPR, TOPR and others also participate. Regardless of the type of security units, in any case, the identification of potential hazards is the basis for planning preventive measures, it allows to define the areas that involve the greatest risk of natural disasters and technical failures. Metodologia: The article uses theoretical research methods, including an analysis of the provisions of the Act on Fire Protection, the Regulation on the Detailed Organization of the National Rescue and Firefighting System, the provisions of the Constitution of the Republic of Poland, the Act on Volunteer Fire Brigades and the acts regulating the status and tasks of local government. The article discusses the structure and tasks of the national rescue and firefighting system, with particular emphasis on the cooperation of KSRG with the local government units. The publication also formulates de lege ferenda postulates. While presenting proposals to improve the cooperation of entities operating within KSRG, the authors also used their knowledge and experience gained during many years of work and service for fire protection. Wnioski: The national rescue and firefighting system is an integral part of our country’s public safety system. The experience of recent years has confirmed that it is an effective guarantee of the safety of people and property, while its structure forms a coherent system of rescue forces and resources centred around various entities. Constant technological and political-economic changes impose the need to improve the system and adapt it to current social needs. Therefore, the development of KSRG still requires setting new tasks and looking for solutions to effectively achieve the goals of the system. An important element to improve the operation of KSRG is the construction of the best possible form of cooperation between the rescue formations of the PSP and OSP and local government, since effective security protection is not possible without the cooperation of government and local administration. Słowa kluczowe: public safety, rescue and firefighting system, local government, fire protection