Fire Emergency Response Research Articles

Simulating Daily Large Fire Spread Events in the Northern Front Range, Colorado, USA

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.

Implementing Fire Emergency Response and Health Behaviour Modules to Elevate Islamic Student Knowledge in Boarding Schools

Implementing Fire Emergency Response and Health Behaviour Modules to Elevate Islamic Student Knowledge in Boarding Schools

Hospital Readiness for Fire Emergency Response in Indonesia: A Literature Study

Hospital Readiness for Fire Emergency Response in Indonesia: A Literature Study

IMPLEMENTATION OF THE FIRE EMERGENCY RESPONSE SYSTEM AT DR. TADJUDDIN CHALID MAKASSAR

The growth and arrangement of existing buildings is often not balanced with the readiness of building infrastructure or gives the impression of forced development so that the resulting physical buildings pay little attention to fire hazards. Fire prevention is a building system that aims to save lives and property from the dangers of fire. Preparedness and handling before a fire occurs is a very important factor in preventing fires. The aim of this research is to see whether the implementation of the emergency response system at RSUP Dr. Tadjuddin Chalid Makassar is in accordance with standards. This research method uses qualitative research with a descriptive approach through in-depth interviews. The informants for this research are the person in charge of K3RS, Head of Sanitation Installation, members of the hospital emergency response team , hospital fire brigade team, hospital management team and Head of Facilities and Infrastructure Maintenance. The research results showed that RSUP Dr. Tadjuddin Chalid Makassar has implemented a fire emergency response system that is in accordance with standards, but it is not yet optimal, such as evaluation of training and education that has not been implemented, in the active fire system there are still APARs that have expired and several hydrants that are not attached with instructions for use, then in the system passive fires, there are still gathering places that are still used as parking lots and there are no emergency stairs specifically for patients who use wheelchairs. Keywords : System, Response, Emergency, Fire, Hospital

Predict Future Transient Fire Heat Release Rates Based on Fire Imagery and Deep Learning

The fire heat release rate (HRR) is a crucial parameter for describing the combustion process and its thermal effects. In recent years, some studies have employed fire scene images and deep learning algorithms to predict real-time fire HRR, which has led to the advancement of HRR prediction in terms of both lightweightness and real-time monitoring. Nevertheless, the development of an early-stage monitoring system for fires and the ability to predict future HRR based on current moment data represents a crucial foundation for evaluating the scale of indoor fires and enhancing the capacity to prevent and control such incidents. This paper proposes a deep learning model based on continuous fire scene images (containing both flame and smoke features) and their time-series information to predict the future transient fire HRR. The model (Att-BiLSTM) comprises three bi-directional long- and short-term memory (Bi-LSTM) layers and one attention layer. The model employs a bidirectional feature extraction approach, followed by the introduction of an attention mechanism to highlight the image features that have a critical impact on the prediction results. In this paper, a large-scale dataset is constructed by collecting 27,231 fire scene images with instantaneous HRR annotations from 40 different fire trials from the NIST database. The experimental results demonstrate that Att-BiLSTM is capable of effectively utilizing fire scene image features and temporal information to accurately predict future transient HRR, including those in high-brightness fire environments and complex fire source situations. The research presented in this paper offers novel insights and methodologies for fire monitoring and emergency response.

Analysis of Fire Emergency Response Readiness at Oil and Gas Company

Introduction: Fire is one of the dangers that is often found in the oil and gas industry. A fire preparedness system is needed by companies to avoid dangers and risks that have the potential to cause damage, injury or death. Objective: This research aims to see the readiness of Fire Emergency Response system. The results of this research can be used as a basis for developing policies and programs Fire Emergency Response work. Method: This research uses the FERRAT Form as the basis for the research. The instrument used in this research is the FERRAT Form. Field observations were carried out to see directly the field conditions and the form filling process. Result: The results of this research show that the company's readiness in implementing the Fire Emergency Response system as a whole has reached an average of <90% with an overall value of 40.33%. Conclusion: It is hoped that with this research, companies can carry out independent evaluations and immediately initiate improvements related to risk-based system design, adequacy levels according to potential fire risks and ensuring emergency response equipment is always in a ready-to-operate and alert condition

Fire emergency response system at PT X based on International Safety Rating System (ISRS)

Fire is the unwanted occurrence of fire. Preparedness in the face of catastrophic fire events is part of the Occupational Safety and Health (K3) standards that must be implemented in the company. PT X is a company engaged in concrete manufacturing. In the production process, many potential hazards, such as fires, can cause losses. This research aims to study the fire emergency response system at PT X. The method used is descriptive research, with data collection done cross-sectionally. The analysis was carried out descriptively by clearly and thoroughly describing the condition of the object of study and comparing it with the applicable regulatory standards. The results showed that PT X had implemented a fire emergency response system and efforts to prevent and overcome fire hazards. PT X's efforts include forming a firefighting team providing fire hazard management facilities such as fire extinguishers, fire blankets, first aid kits, evacuation stretchers, and other infrastructure facilities. PT X has conducted fire emergency response and first-aider training for employees and workers. This research concludes that PT X has implemented an emergency response system well and refers to the International Safety Rating System.

An open flame and smoke detection dataset for deep learning in remote sensing based fire detection

ABSTRACT The early warning of fires is pivotal for preventing substantial economic losses and ecological damage. However, the enhancement of fire model generalization performance still faces challenges due to limitations in existing fire datasets, such as image quantity and heterogeneity. Taking the advantage of rapid advancements in remote sensing technology and artificial intelligence, we introduce the Flame And Smoke Detection Dataset (FASDD), a pioneering collection comprising over 120,000 heterogeneous images covering various fire scenarios, aiming to promote the evolution of fire detection models. FASDD serves as a challenging benchmark for object detection tasks and features three sub-datasets: FASDD_CV, FASDD_UAV, and FASDD_RS. These sub-datasets comprise cross-domain images from ground-based, airborne, and spaceborne sensors, respectively. Extensive experiments using advanced Swin Transformer models demonstrate satisfactory fire detection performance, with mAP scores of 84.9%, 89.7%, and 74.0% on the respective sub-datasets. Notably, the application case in wildfire localization highlights the proficiency of FASDD_RS models for monitoring and tracking forest fires. Trained on FASDD_RS, FASDD_UAV, and FASDD_CV, deep learning models can be individually deployed on edge devices including satellites, drones, and terrestrial sensors, thus empowering collaborative fire warning within the space-air-ground integrated observation network. Additionally, a standardized procedure for data preparation, data procedure, and quality assurance is established, providing off-the-shelf annotations with four common configurations to facilitate machine learning model training. FASDD stands as a pivotal benchmark, providing valuable resources for training and evaluating deep learning fire detection models, especially Large Vision Models (LVM). It holds the potential to accelerate research progress in urban firefighting or forest fire detection, providing vital security measures for early fire warning and emergency response tasks.

User-Centered Fire Emergency Response System Design for Presenting BIM and IoT Data

Advanced technologies, such as Building Information Modeling (BIM) and the Internet of Things (IoT), can supply information for decision-making during a fire emergency. However, the data retrieved fails to cover all the critical knowledge required for situational awareness or fails to present to the users in a usable and effective way. This study aims to develop an effective user interface for a Fire Emergency Response System (FERS) based on BIM and IoT technologies to provide the information required for situational awareness. The study uses the User-Centered Design (UCD) method to achieve this aim and develops the Graphical User Interface (GUI) of the FERS based on the identified information requirements. To evaluate the usability of the developed GUI and to finalize the design, the feedback from the experts was retrieved via recordings when they were using the prototype and a combined usability questionnaire of Usability Metric for User Experience (UMUX) and After-Scenario Questionnaire (ASQ). The findings illustrate that the GUI successfully presented the information to meet the user goals and has the potential to significantly improve the efficacy of fire responding operations and minimize the risk of loss of life and property damage.

The Effects of Job Characteristics of Firefighters Dispatched to Disaster Sites on Job Satisfaction

This empirical study investigates how the job characteristics of firefighters, who provide immediate fire suppression, rescue, and emergency response services when a disaster occurs, affect their job satisfaction. Through a literature review, we evaluated the job characteristics of firefighters dispatched to disaster sites according to their levels of “risk”, “urgency”, “uncertainty”, “ambiguity” and “shift work” and selected 296 firefighters involved in fire suppression, rescue, and first aid. We surveyed the subjects to verify the causal relationship between their job characteristics and job satisfaction. Uncertainty and shift work were found to have a statistically significant negative (−) effect on job satisfaction, and urgency was found to have a positive (+) effect. On this basis, an efficient and effective personnel policy plan for the fire service organization was presented.

Pelatihan pencegahan dan penanggulangan bahaya kebakaran di fire station Bandar Udara Internasional Soekarno-Hatta

The importance of awareness of fire risks in the community, especially at airports, encourages the need for effective prevention and countermeasures. Community service by lecturers of the D.III PKP Study Program through this training aims to provide knowledge and skills to the community of airport service users, especially workers in tenants, regarding how to prevent and overcome fires. Involving the Tri Darma Perguruan Tinggi concept, this training not only touches on aspects of fire prevention but also discusses fire phenomena, risk analysis, emergency response, and fire management. It is hoped that training participants will benefit them through increased knowledge and understanding that can increase awareness of fire risks so that the community around Soekarno-Hatta International Airport can be better prepared and trained in dealing with potential fire hazards.

Charging rate effect on overcharge-induced thermal runaway characteristics and gas venting behaviors for commercial lithium iron phosphate batteries

Increasing charging rate is an upgrading direction of electrochemical energy storage, which might induce more heat accumulation, posing a higher risk to cause the battery thermal runaway (TR). Driven by this, an experimental investigation was carried out to study the characteristics of TR and gas venting behaviors in commercial lithium iron phosphate (LFP) batteries that were induced by overcharging under different rates. As the charging rate increases, the growth rate of lithium dendrites accelerates, resulting in the earlier formation of large-scale short circuits and a rapid transition to the next stage of the TR chain reaction. However, the temperature rise and the total heat does not increase in proportion to the charge rates. The electrolyte also accelerates its decomposition, and the reaction between the electrolyte and lithium is the main reason of overcharge heat accumulation. It is worth noting that the charging rate for overcharging is positively correlated with the TR violence of batteries and the explosion risk of the ejected gas. The main gases emitted are H2, CO, and CO2 and various alkanes. Specifically, the content of H2 gas increases significantly with the increase of charging rate. And the H2 contents for 2C, 1.5C and 1C correspond to 61%, 53% and 50%, respectively. The rapid growth of lithium dendrites results in a significant increase in H2 generation and earlier capture. This is accompanied by a higher impact pressure and a lower explosion limit (LEL) of the combustible gas mixture, indicating a severer explosion risk. In short, by comparing the TR characteristics and gas products of LFP batteries with different charging rates, which is of great significance for the fire extinguishing design and emergency response of energy storage systems.

Enhancing Fire Emergency Response Readiness in Butiama District Secondary Schools in Tanzania: An in-depth Investigation

Fire hazards in secondary schools are a global concern, impacting countries worldwide with varying rates and consequences. Tanzania, in particular, has grappled with recurring fire disasters, with schools bearing a significant brunt of these incidents. While government policies and circulars have mandated safety measures like smoke detectors, emergency exits, and the prohibition of candles and crude oil lamps in schools, compliance remains a persistent issue. This study is firmly grounded in three influential theories: Abraham Maslow's hierarchy of needs theory, the protection motivation theory, and modern disaster theory. Our study used a descriptive research design based on a mixed research approach, combining qualitative and quantitative methodologies. Data collection involved questionnaires, interviews, and observations. One of the significant findings is the glaring lack of awareness among both teachers and students regarding fire emergency preparedness and the lack of training in fire safety preparedness. The study also delves into the root causes of fire emergencies, which include poor electrical appliance installation, ignorance about fire materials, negligence, and deliberate sabotage. These findings highlight the vulnerabilities that schools face and emphasize the critical importance of preparedness measures. Financial constraints and a lack of fire safety resources emerge as formidable challenges. To bolster fire emergency response readiness, the study proposes a range of strategies. These include the development of comprehensive fire emergency plans, increased investment in fire safety education, procurement of essential fire safety equipment, the establishment of school dispensaries, budget allocation for preparedness efforts, and the promotion of regular fire drills.

Multipurpose deep learning-powered UAV for forest fire prevention and emergency response

This paper presents a customized UAV designed for rescue and safety purposes in the forest sector. The UAV features a durable F450 frame quadcopter with four 1000KV brushless motors and a KK2.1 Flight Control Board for stability and manoeuvrability with a runtime of 90 min. It incorporates a Raspberry Pi camera for real-time video streaming, enabling efficient identification of individuals in need of assistance. The GSM module allows contactless communication, ensuring streamlined and safe interaction. A motor controls the lid of the customizable first aid kit box, facilitating efficient aid delivery. The Neo-6 M GPS module provides accurate localization of the drone and individuals in distress with a horizontal position accuracy of 2.5 m. The UAV collects temperature and humidity data using the DHT 11 sensor having +/- 2 degreesC and +- 5% accuracy respectively. This sensor employs advanced deep learning models, including artificial neural networks (ANN) and generative adversarial networks (GANs), for real-time forest fire prediction with an accuracy of 90.7 % The integration of GANs enhances accuracy through synthetic data generation. Moreover, all these components are interfaced using a Raspberry Pi4 and a GUI, providing a smooth user control experience and end-to-end information for quick and effective emergencyresponse.

Study on the Location Determination of Building Fire Points Based on Acoustic CT Temperature Measurement

Rapid perception of the location of the fire point is crucial to building fire emergency response in the process of building fire emergency response, which can help firefighters direct fire-fighting operations, effectively control fire sources, and provide strong evidence for the analysis and investigation of fire causes. This paper uses acoustic CT temperature measurement technology to determine the fire source location of a building fire and verifies its validity and applicability as follows: we construct various fire point numerical models based on the fire dynamics simulator (FDS) and obtain temperature data at different times; neural network means were used to obtain the time-of-flight (TOF) of an acoustic wave traveling; the large ill-conditioned matrix equation of acoustic flight under different meshing schemes was constructed and solved based on the simultaneous algebraic reconstruction technique (SART) and least squares QR-decomposition (LSQR), and then reconstruction temperature data under each scheme were obtained. Through the error analysis, the reconstruction effect of each reconstruction scheme is evaluated, and then the applicability of the location coordinate determination of the fire point is analyzed. The results show that the determination of the fire location under the conditions of various fire points in the building space can be realized by acoustic CT temperature measurement technology.

Effect Of Educational Media Picture Card On Knowledge Of Fire Disaster Response In School-Age Children

Background: Every year cases of fire disasters have increased. Fire disasters can occur anywhere resulting in victims and loss of property. Fire disasters can occur in adults, the elderly and also children. Children are included in communities that are often at risk of the impact of fire disasters, because their skills and knowledge are still considered to be lacking which is the reason children become victims. Prevention efforts that can be done by providing education as early as possible to children about fire emergency response, one of the media that can be used for school-age children is media picture cards in the form of pictures and explanations that are easy for school-age children to understand and remember. Research Objectives: To determine the effect of educational media picture cards on knowledge of fire emergency response in school-age children. Research Method: Type of quantitative research with a Pre-Experimental approach using the One Group Pretest-Posttest Design. The sample involved 74 respondents by selecting the sample with the Total Sampling technique. The research instrument uses picture cards and knowledge questionnaire sheets about fire emergency response. Data analysis using the Wilcoxon statistical test. Results: The median value before being given education was 14.00 and after being given education it was 19.00 so that there was a significant difference between before and after education on picture card media on fire disaster emergency response knowledge obtained value (P Value = 0.001). Conclusion: There is an educational effect of picture card media on knowledge of fire emergency response in school-age children.

Enabling fire source localization in building fire emergencies with a machine learning-based inverse modeling approach

During building fire emergency response (ER), one of the most important tasks for firefighting is to locate the fire source. Currently, incident commanders on the fire ground determine the origin of fire sources, relying mostly on the reports of firefighters entering the building or the information from on-site witnesses. This inevitably requires firefighters to approach the fire origin and observe, causing significant dangers and risks. To overcome this issue, an inverse model for building fire source localization is developed. A machine learning (ML)-based inverse modeling approach is used to build the complex relationship between the fire source location and on-site temperature sensor measurements, so that an inversion of the fire source based on the temperature observations can be enabled. By taking fire in an actual building floor as a case study, the inverse model for fire source localization using simulated fire temperature data is established. ML methods of feed-forward neural networks (FFNNs) and long short-term memory (LSTM) are respectively employed. Results indicate that LSTM in stateless mode is more appropriate to establish the relative models because more accurate recognition of the fire source room can be achieved during the initial and growth stages of fire development. Besides, by considering different means of on-site data acquisition in building fire emergencies, fire source localization via stationary temperature sensor arrays and portable temperature measuring devices is realized. The formulated models achieve recognition accuracy of 99.5% (direct recognition) and 92.1% (in top-3 candidates) in the first 15 min of the fires, respectively. The effectiveness of using the ML-based inverse modeling technique for fire source localization in building fire emergencies is demonstrated.

HUBUNGAN PENGETAHUAN PERAWAT RUANG RAWAT INAP TENTANG PENGGUNAAN APAR (ALAT PEMADAM API RINGAN) DENGAN TERJADINYA RESIKO BENCANA KEBAKARAN DI RUMAH SAKIT UMUM SEMBIRING DELITUA

The hospital is one of the high-rise buildings that have the potential for fires originating from electricity, stoves, and storing flammable materials for example fuel and medical gas and installation of power plants such as generator sets which are the source of fires. Fire is an unwanted and uncontrollable occurrence of fire. The purpose of this study was to examine the relationship between knowledge of inpatient nurses regarding the use of fire extinguishers with the occurrence of fire disaster risk at Sembiring Delitua General Hospital.The design of this study used an observational analytic method through a cross sectional approach. The study population were 158 people, with sample of 61 nurses used the proportional stratified random sampling method. The research instrument was questionnaire. Data Relationship between Inpatient Nurse Knowledge about the Use of Fire Extinguishers and the Occurrence of Fire Disaster Risk in the Post-Covid-19 Pandemic at Sembiring Delitua Hospital.analysis methods were univariate and bivariate with chi square test. The results showed that from 61 nurses, 47 people (77%) had poor knowledge about the use of fire extinguishers and resulted in a high risk of fire disaster and 14 people (23%) have good knowledge about the use of fire extinguishers and with a low risk of fire disasters. The results of the univariate analysis showed that there was a significant relationship between nurses' knowledge of the use of fire extinguishers and the occurrence of fire disaster at Sembiring General Hospital with p = 0.003 < 0.05. Conclusion: nurses' knowledge about the use of fire extinguishers has a relationship with the occurrence of fire disaster risk with 4,170 times greater chance. Suggestion: the management provides training and simulations to health workers such as nurses on how to use fire extinguishers and fire emergency response.

Fire Safety Management Strategies in Selected Markets of Lusaka City

This study investigated the effectiveness of fire safety management strategies in selected markets of Lusaka city. The study employed a descriptive research design, which involved qualitative data collection and analysis. The study used purposive sampling to select markets and participants for the study. A total of 2 markets. From each market, 10 marketeers were sampled conveniently. 2 market managers were also sampled purposefully. The study collected data using two main methods: Focus Group Discussions (FGD) and in-depth interviews. The marketeers were interviewed individually and FGD guide was further administered to get more insights. This was meant to collect opinion and insights that could not be collected during interviews. The in-depth interviews were conducted with a subset of the participants to obtain more detailed and nuanced information. The data collected from the in-depth interviews and FGD were analyzed using thematic analysis to identify patterns and themes in the data. The findings of the study indicated that effective fire safety management in markets required a combination of proactive measures and emergency preparedness. Proactive measures included regular risk assessments, proper storage of flammable materials, regular maintenance of electrical systems, and the installation of fire safety equipment such as alarms and sprinklers. Emergency preparedness strategies included the creation of emergency evacuation plans, the establishment of communication protocols, and the training of market staff on fire safety procedures. The study also found that collaboration between market management, vendors, and local fire departments is crucial to effective fire safety management in markets. The study highlights the need for ongoing education and training for vendors and market staff to maintain awareness of fire safety hazards and emergency response procedures. It also underscores the importance of regular reviews and updates of fire safety management plans to ensure they remain effective and relevant. Overall, the study provides valuable insights into effective fire safety management strategies in markets, and emphasizes the need for continued attention and investment in fire safety management to protect people, property, and businesses from the devastating effects of fires.

Lessons Learned from an OB / Newborn / Neonatal Intensive Care Full-Scale Exercise

Introduction:Children are frequently victims of disasters. However, gaps remain in disaster planning for pediatric patients. The New York City Pediatric Disaster Coalition (NYCPDC) is funded by the NYC Department of Health and Mental Hygiene (DOHMH) to prepare NYC for mass casualty incidents that involve large numbers of children.On April 26, 2018, the NYC PDC conducted a first in NY, full-scale exercise with the NYC Fire Department (FDNY) testing evacuation, patient tracking, communications, and emergency response of the Obstetrics, Newborn and Neonatal units at a NYC based hospital. The goal of the exercise was to evaluate current Ob/Newborn/Neonatal plans and assess the hospital’s ability to evacuate patients.Method:The exercise planning process included a review of existing OB / Newborn / Neonatal plans, four group planning meetings, as well as, targeted specific area meetings and plan revisions. The exercise incorporated scenario-driven, operations-based activities, which challenged participants to employ the facility's existing evacuation plans during an emergency.Results:The Exercise assessed the following: Communication, Emergency Operation Plans, Evacuation, Patient Tracking, Supplies and Staffing. Internal and external evaluators rated exercise performance on a scale from 1-4. Evaluators completed an exercise evaluation guide based on the Master Scenario Event List.An After Action Report was written based on the information from the exercise evaluation guides, participant feedback forms, hot-wash session, and after action review meeting. Strengths included the meaningful improvement of plans before the exercise (including the fire department) and the overall meeting of exercise objectives.Conclusion:Lessons learned included: addressing gaps in effective internal and external communications, adequate supplies of space, staff, equipment needed for vertical evacuations; providing staging and alternate care sites with sufficient patient care and electrical-power resources. The lessons learned are being utilized to improve existing hospital plans to prepare for future full-scale exercise and or real-time events.