Fire Risk Management Research Articles

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

Assessing and mitigating building fire risk based on the scenario clusters in Bangladesh

The increasing occurrence of fire hazards in residential buildings in Bangladesh has led to severe property damage, physical injuries, and fatalities, underscoring a critical research gap in effective fire risk management. This study aims to address this gap by identifying key fire risks and proposing mitigation strategies using BIM technology. The research employs a comprehensive survey and scenario clustering methodology to identify these risks and assess the efficacy of various fire-resistant materials. Data collection involved questionnaires distributed to 200 respondents, including building owners, occupants, engineers, and firefighters. These clusters are based on the metrics of fatality numbers and property damage to quantify fire risk. Findings highlight the significant role of wall and ceiling lining materials (RII = 0.919) in contributing to fire risk. Further analysis was conducted using a residential building model developed in Autodesk Revit and simulated in Pyrosim software, assessing various lining materials. Quantitative results demonstrated that fire brick is the most effective material, exhibiting the lowest heat release rate (968 kW/m2), minimum adiabatic surface temperature (28°C), minimum heat transfer coefficient (1.75 W/m2/K), and lowest surface burning rate (7.5e−08 Kg/m2/s). These results suggest that fire brick is the optimal choice for enhancing fire resistance in residential buildings.

Fire messaging: a qualitative exploration of how adults teach children about fire

Purpose Research indicates that children’s early experience of fire can influence what and how they learn about fire. In turn, early fire-learning can influence how people come to use it later in life, including whether they engage in maladaptive use, i.e. firesetting. Little is known about the underlying mechanisms of fire-learning, which was the basis for this study. The research question was: “how do adults educate children about fire in the UK/ Ireland”? The purpose of this study was to explore the child fire education process from an adult perspective. Design/methodology/approach Semi-structured interviews was conducted with 21 fire-educators who had regular contact with children. Data was analysed using abbreviated grounded theory. Steps were taken to ensure reliability and validity, including a Cohen’s Kappa calculation, indicating an agreement level of 0.9 between two raters. Findings Four core themes were identified: the fire narrative, enabling growth, risk management, and communication, from which the fire-learning process model (F-LPM) was formed. Research limitations/implications The main limitation is the nature of the sample, in that it was diverse and self-selecting. Practical implications The findings are discussed, including how they offer support for social learning theory (SLT) perspectives on fire-learning. Social implications The social construction of fire (i.e. the way fire is viewed by society as a whole in the UK/ Ireland) is discussed in depth, and cultural variability is highlighted. Suggestions are made for how the societal view of fire and, consequently, how adults convey this to young people, could be better balanced. Originality/value To the best of the authors’ knowledge, this is the first study in the UK/ Ireland to consider how adults educate young people about fire from a concurrent perspective. This is important given that research indicates early experiences of fire can impact how people later go on to use it (including maladaptive use in the form of firesetting).

Assessing forest fire dynamics and risk zones in Central Indian forests: a comparative study of the Khandwa and North Betul forest divisions of Madhya Pradesh.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

Escalating Wildfires in Siberia Driven by Climate Feedbacks Under a Warming Arctic in the 21st Century

AbstractSiberian wildfire is of paramount importance in the carbon cycle and climate change as it is a major disturbance in the pan‐Arctic ecosystems. In recent decades, the Siberian wildfire regime has been shifting; however, less is known about its process‐based feedback mechanisms. By integrating in‐situ and satellite observational data sets as well as chemistry‐climate coupled modeling, we find that central Siberia has featured the most prominent wildfire escalation during the past two decades, which is closely related to hydrological drought with decreasing rainfall and drying soil under a fast‐warming Arctic. Furthermore, fire‐emitted aerosols compound the increasing wildfires via serving as cloud condensation nuclei and suppressing precipitation, forming self‐amplifying feedback. As the Arctic warming is projected to continue, wildfires are estimated to more than double by the end of this century. This work highlights the great importance of fire risk management based on a fundamental scientific understanding of the complex climate system.

Challenges of pre-hospital emergency care at Addis Ababa Fire and Disaster Risk Management Commission, Addis Ababa, Ethiopia: a qualitative study

BackgroundA challenge to pre-hospital emergency care is any barrier or obstacle that impedes quality pre-hospital care or impacts community pre-hospital utilization. The Addis Ababa Fire and Disaster Risk Management Commission (AAFDRMC) provides pre-hospital emergency services in Addis Ababa, Ethiopia. These services operate under a government-funded organization that delivers free emergency services, including out-of-hospital medical care and transportation to the most appropriate health facility. This study aimed to assess the challenges of pre-hospital emergency care at the Addis Ababa Fire and Disaster Risk Management Commission in Addis Ababa, Ethiopia.MethodsA qualitative descriptive study was conducted from November 20 to December 4, 2022. Data were collected through in-depth, semi-structured interviews with 21 experienced individuals in the field of pre-hospital emergency care, who were selected using purposeful sampling. A thematic analysis method was used to analyze the data.ResultsThis study includes twenty-one participants working at the Addis Ababa Fire and Disaster Risk Management Commission. Three major themes emerged. The themes that arose were the participants’ perspectives on the challenges of pre-hospital emergency care in Addis Ababa, Ethiopia.Conclusion and recommendationThe Fire and Disaster Risk Management Commission faces numerous challenges in providing quality pre-hospital emergency care in Addis Ababa. Respondents stated that infrastructure, communication, and resources were the main causes of pre-hospital emergency care challenges. There has to be more focus on emergency management in light of infrastructure reform, planning, staff training, and education, recruiting additional professional power, improving communication, and making pre-hospital emergency care an independent organization in the city.

Analytical formulations with experimental validation for determining the incident heat flux from firefronts

Any target exposed to a heat source receives a certain amount of heat flux, depending on the distance, view factor, characteristics of the source, and environmental properties. Incident heat flux is mainly characterized by firefront properties, e.g., flame dimensions, and its thermal attributes by fuel properties, topography, and meteorological factors. Predicting the incident heat flux from the firefronts is crucial for fire risk management and analytical and numerical fire modelling. Based on this specification, safe zones can be defined for elements such as unprotected individuals, firefighters, and buildings. Since the contribution of radiation and convection varies with the fire evolution, it is essential to specify them as a function of distance and slope. This study elaborates determination of radiation and convection for firefronts. Sets of laboratory-scale experiments were conducted using fuel beds of straw with a load of 1.5 kg/m2 on the slopes of 0°, 20°, and 40° measuring the incident radiative and total heat fluxes. The proposed methodology was validated using available approaches and correlations between the flame length and the acceptable safety distance (ASD) were established. Unlike the existing models, a nonlinear correlation between convection and radiation during the firefront evolution is proposed.

Conceptual Model for Integrated Meso-Scale Fire Risk Assessment in the Coastal Catchments in Croatia

Various factors influence wildfire probability, including land use/land cover (LULC), fuel types, and their moisture content, meteorological conditions, and terrain characteristics. The Adriatic Sea coastal area in Croatia has a long record of devastating wildfires that have caused severe ecological and economic damages as well as the loss of human lives. Assessing the conditions favorable for wildfires and the possible damages are crucial in fire risk management. Adriatic settlements and ecosystems are highly vulnerable, especially during summer, when the pressure from tourist migration is the highest. However, available fire risk models designed to fit the macro-scale level of assessment cannot provide information detailed enough to meet the decision-making conditions at the local level. This paper describes a model designed to assess wildfire risks at the meso-scale, focusing on environmental and anthropogenic descriptors derived from moderate- to high-resolution remote sensing data (Sentinel-2), Copernicus Land Monitoring Service datasets, and other open sources. Risk indices were integrated using the multi-criteria decision analysis method, the analytic hierarchy process (AHP), in a GIS environment. The model was tested in three coastal catchments, each having recently experienced severe fire events. The approach successfully identified zones at risk and the level of risk, depending on the various environmental and anthropogenic conditions.

Next-gen regional fire risk mapping: Integrating hyperspectral imagery and National Forest Inventory data to identify hot-spot wildland-urban interfaces

The increasing threat of high-severity wildfires in Mediterranean Wildland-Urban Interface (WUI) areas demands to develop effective fire risk assessment and management strategies. Simultaneously, the newfound accessibility of spaceborne hyperspectral data represents a significant potential for generating fire severity assessments, whereas National Forest Inventories (NFI) offer a vast dataset related to vegetation and fuel loads, which is essential for shaping the planning and strategies of forest services. This research work aims to advance the state-of-the-art in WUI fire risk mapping in the western Mediterranean Basin by combining PRISMA spaceborne hyperspectral data and Spanish NFI data. The proposed methodology had three main stages: (i) fire severity assessment at local scale (a wildfire) by using PRISMA hyperspectral data and Multi-Endmember Spectral Mixture Analysis (MESMA) leveraging field-based measurements of the Composite Burn Index (70 plots); (ii) development of a high fire severity probability map at regional scale from the extrapolation of a Random Forest predictive model calibrated from fire severity estimates, NFI data and topo-climatic variables at local scale (overall accuracy = 92 %; Kappa = 0.8); and (iii) identification and characterization of zones that concentrate WUIs with high probability of high fire severity if a fire event occurs (hot-spot WUIs) by crossing the information from the previous regional high fire severity probability map and a WUI cartography developed at regional scale. Study area was Castilla y León Autonomous Region (larger Spanish region, 94,226 km2), where the second-largest extreme Spanish wildfire event (28,000 ha) occurred. We identified hot-spot WUIs so that stakeholders and decision-makers could (i) prioritize resources and interventions for effective fire management and mitigation, (ii) allocate resources for prevention, and (iii) plan evacuation measures to safeguard lives and property. This study contributes to the development of next-generation fire risk assessment methods that combine remote sensing technologies with comprehensive ground-level datasets.

The 2023 wildfire season in Québec: an overview of extreme conditions, impacts, lessons learned and considerations for the future

The 2023 wildfire season in Québec set records due to extreme warm and dry conditions, burning 4.5 million hectares and indicating persistent and escalating impacts associated with climate change. This study reviews the unusual weather conditions that led to the fires, discussing their extensive impacts on the forest sector, fire management, boreal caribou habitats, and particularly the profound effects on First Nation communities. The wildfires led to significant declines in forest productivity and timber supply, overwhelming fire management resources, and necessitating widespread evacuations. First Nation territories were dramatically altered, facing severe air quality issues and disruptions. While caribou impacts were modest across the province, the broader ecological, economical, and social repercussions were considerable. To mitigate future extreme wildfire seasons, the study suggests changes in forest management practices to increase forest resilience and resistance, adapting industrial structures to changes in wood type harvested, and enhancing fire suppression and risk management strategies. It calls for a comprehensive, unified approach to risk management that incorporates the lessons learned from the 2023 fire season and accounts for ongoing climate change. The studyunderscores the urgent need for detailed planning and proactive measures to reduce the growing risks and impacts of wildfires in a changing climate.

Fire and explosion risks in production workshops within craft village industrial clusters: A study in Huu Bang commune, Thach That district, Hanoi

Effectively managing risks will help businesses control and minimize damages when risks occur by timely implementing appropriate and pre-prepared response solutions. However, in industrial zones in general and craft village industrial clusters in particular, risk management and fire prevention have not been well implemented, leading to many fire and explosion incidents, causing significant human and material losses, pushing businesses into difficulties and losses. Researching and investigating fire prevention and risk management in explosions helps businesses in craft village clusters gain awareness of fire and explosion risks, understand the causes, and have methods to reduce the risk of fire and explosion in their production workshops. It also disseminates preventive measures for fire and explosion risks to all businesses in the woodworking craft village cluster of Huu Bang Commune, Thach That District, Hanoi.

Geographic Information System (GIS) Spatial Analysis to Detect the Distribution Pattern of Unused Land Fires at Ogan Ilir Regency

Land fires pose a serious threat to the environment and communities in Ogan Ilir Regency, South Sumatra. This study aims to analyze the distribution pattern of land fires using GIS spatial analysis technology. Through Sentinel-2 image classification and fire intensity analysis, it had been identified focal points of dormant land fires mainly in South Indralaya, North Indralaya, and West Pemulutan sub-districts. The results of this study provide in-depth insight into the spatial distribution of land fires and the factors that influence their occurrence. This research also produced a map of the distribution of fire hotspots in unused land areas. From the research, it can be concluded that spatial analysis can map in detail and comprehensively the burning locations on unused land. The implication of this research is the need to develop more effective mitigation strategies to address the risk of land fires in the future. Therefore, this study not only makes an important contribution to the scientific literature but also offers new directions for research and policy focused on sustainable and effective land fire risk management.

Safe Health-Care Environment Advisory Program Counseling Experience Sharing

<p>文以醫療機構環境與基礎建設各系統現場訪視，採系統追蹤方式，從受訪2家醫療院所環境風險管理與火災風險辨識著手，以消防安全設施設備管理；各樓棟防火區劃完整度檢視；避難逃生路徑規劃與逃生方向指引；醫用氣體使用安全；危險物質儲存、使用、洩漏管理；用電與各機房環境安全及緊急災害應變措施機制等面向，重點檢視受輔導醫療機構設施設備與環境安全現況及安全脆弱點，提供醫療機構管理階層，提升病人就醫環境整體安全之參考。</p> <p> </p><p>This paper describes a systematic tracking approach for conducting on-site inspections at two medical facilities and assessing their institutional environments and infrastructure. The assessment centers on the management of environmental and fire risks, including overseeing fire safety facilities and equipment. The comprehensiveness of the fire zoning of individual buildings and the planning of and guidance to evacuation routes are evaluated. Also assessed are the safe use of medical gases, the management (including storage, use, and leakage) of hazardous substances, electrical safety, environmental safety in rooms with heavy machinery, disaster response, and emergency management. The primary objective is to scrutinize the safety vulnerabilities of the facilities and to critically evaluate the general environmental conditions at the two medical institutions, providing management teams with insights to enhance the safety of the health-care environment.</p> <p> </p>

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Forest fires in Türkiye have devastated 2.5 million hectares of habitat over four decades, posing a grave threat to Mediterranean forest ecosystems. This study compares machine learning techniques: Decision Trees (DT), Naive Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), for predicting forest fire vulnerability. Using a dataset encompassing various factors like precipitation, soil moisture, temperature, humidity, wind speed, land cover, elevation, aspect, slope, proximity to roads/electricity networks, and population density, the models were trained and tested. The dataset classified vulnerability into four classes: very low, low, moderate, and high. Evaluation metrics included overall accuracy, precision, sensitivity, F1-score, Cohen kappa, and cross-validation (CV).RF exhibited the highest performance (accuracy: 0.80, precision: 0.78, sensitivity: 0.80, F1-score: 0.78, Cohen kappa: 0.71, average CV: 0.71), predicting fire vulnerability classes very low (14.99%), low (0.68%), moderate (65.41%), and high (18.90%) with notable accuracy. DT yielded consistent results, while NB performed stably, though slightly lower than RF and DT. However, ANN and SVM demonstrated lower performance and higher variability. These findings advocate for RF as the most accurate algorithm for forest fire risk prediction, emphasizing its crucial role in proactive fire risk management strategies.

Mitigating land fire disasters: Implementation of agricultural land fire risk management information system in Aunupe Village, South Konawe Regency

Climate change, which causes increased temperatures, prolonged droughts, and dry conditions, heightens the risk of land fires. A similar scenario unfolded in Aunupe Village, where several fires occurred on agricultural land over the past three years, resulting in crop failure and degraded soil quality, leading to financial losses for land-owning farmers. To mitigate the risk of land fire disasters, an agricultural land fire disaster risk management information system is necessary in Aunupe Village, South Konawe Regency. The objective of this community-based empowerment program is to mitigate the risk of land fire disasters. To achieve the desired outcomes, initial coordination with farmer group partners is conducted, followed by the design and installation of fire sensor equipment sets in collaboration with farmer groups. Furthermore, focus group discussions (FGD) are conducted with farmer group partners to ensure their understanding and cooperation. The activities result in the development of an agricultural land fire risk management information system, facilitating the prompt detection, monitoring, and response to fire incidents in the agricultural land of the Lembah Harapan Farmers Group. Additionally, farmer group partners acquire knowledge regarding the utilization of fire mitigation systems.

Fires and fire risk perception predictors on Finnish farms

ABSTRACT The physical and operational farm environment has become more complex, challenging risk management. This study aimed to identify the number and typical features of farm fires and the factors connected to farmers’ assessments of fire risk. The data comprised a subset extracted from the national accident database of the Finnish rescue services from 2000 to 2021 (3,989 farm fire alarm calls) and farm survey data collected in the spring of 2021 (506 valid responses). The descriptive analysis of the fire incident data showed that the most typical fire sites on farms were dairy cattle buildings, grain dryers, and heating plants. Most farmers (83.4%) assessed the probability of fire as intermediate or high. The Bayesian machine learning approach used to analyse the survey data suggested the control of malfunctions, failures of machinery and equipment, and educational efforts were key elements in fire risk management.

Development and validation of a soft voting-based model for urban fire risk prediction

Early identification of fire risk helps to mitigate the possible consequences incurred by fire, and meanwhile helps optimize the allocation of rescue resources. In this paper, we propose a comprehensive machine learning-based approach for predicting fire risk. The study area is divided into multiple 1 km × 1 km grid cells, and fire incident records, Point of Interest (POI) data, and meteorological data are geographically mapped to these grid cells. By applying the k-means clustering algorithm to analyze monthly fire occurrences, fire risk levels are assigned to each grid cell. A dataset is constructed with the target variable being the fire risk level and the predictive variables being POI data and meteorological data. After undergoing feature engineering, the dataset is divided into training and testing sets based on a time span. Deep neural networks, Randomforest, and Extreme Gradient Boosting (XGBoost) algorithms are trained on the training set and combined using a soft voting approach to create the final soft voting-based model (SV-based model). The performance of the SV-based model is rigorously evaluated on the testing set, achieving a prediction accuracy of 98.9 %. When evaluated using the AUC and F1-score as metrics, the scores are 0.92 and 0.98, respectively, surpassing other machine learning models used for comparison. The findings of this research contribute to the field of urban fire risk management and provide valuable insights for decision-makers and urban planners in formulating proactive fire prevention strategies.

Validating MCD64A1 and FireCCI51 burned area mapping in the Qinghai-Tibetan Plateau

As an alpine area, surface fires significantly affect the fragile ecosystems of the Qinghai-Tibetan Plateau (QTP). However, no studies on the accuracy of any remote sensing burned area (BA) products on the QTP has not been evaluated, resulting in higher uncertainties in BA mapping. To address this, we used 56-view medium-resolution Landsat images of the QTP from 2001 to 2020 to verify the MCD64A1 and FireCCI51 products by stratified sampling. The results showed that MCD64A1 was more accurate than FireCCI51 for identifying small-scale (<2 km2) BAs within the QTP, whereas FireCCI51 was more accurate for identifying medium-scale (2–5 km2) and large-scale (>5 km2) BAs. Additionally, MCD64A1 was more accurate than FireCCI51 for identifying BAs in forests, FireCCI51 was more accurate than MCD64A1 for identifying BAs in grasslands, shrublands, and wetlands. These results can aid fire risk assessment in the region to improve ecosystem stewardship and inform further improvements in BA products.

Wildfire risk weighting and behaviour prediction using open geospatial data and ontologies

This article presents a novel approach to wildfire risk assessment and behaviour prediction by leveraging open geospatial data and ontologies. The proposed methodology includes a spatially weighted index model and multicriteria analysis to represent the risk of forest fires in the affected area. It bridges gaps in theory and practice, offering a comprehensive solution for evaluating potential forest fire risk in near real time, predicting fire behaviour and elucidating the semantics of fire management. During dry and hot conditions, forest fires tend to escalate. Hence, we propose an algorithm that combines experts’ empirical criteria and open-source data to identify dangerous fires in near real time, aiding authorities in directing attention to the riskiest areas. The objective is to predict forest fire behaviour, a complex and nonlinear system influenced by dynamic factors such as weather conditions, topography and land use. Our methodology enables real-time assessment of potential forest fire risks, complemented by predictive fire behaviour scenarios and a descriptive ontology of fire management semantics. We examine existing fire-related ontologies and propose a comprehensive one encompassing incident descriptions, firefighting resources, actor interrelations and knowledge for effective action. By classifying fire sources, our algorithm enables strategic decision-making to prevent uncontrolled fires. This solution significantly enhances data using semantic and spatial relationships among wildfire resources. Furthermore, we demonstrate how ontologies improve data integration and interoperability among diverse systems and organisations involved in forest fire risk management, fostering better coordination and faster responses to critical situations. To facilitate decision-making, we create decision-making scenarios linked to analysed hot spots, drawing from open hot spot data such as National Aeronautics and Space Administration (NASA) Fire Information for Resource Management System (FIRMS), OpenStreetMap (OSM), OpenWeatherMap (OWM) and OpenTopoData (OTD). We propose an ordinal and linguistic classification system (F1–F5) denoting risk levels as low, moderate, high, very high and extreme. These values are obtained through factor aggregation and fuzzy logic. A publicly accessible, interactive web map displays the results derived from this model. Overall, our contributions to wildfire risk management provide authorities with a valuable tool to make informed decisions and mitigate the damaging effects of wildfires.

Harmonization of semi-objective ANP with explained CRITIC for quantitative evaluation of fire hazard risks for flame-retardant materials

The fire hazard assessment of polymers and the effect of flame retardants constitutes the foundation for preventing and managing fire risks to public safety. This study addresses key challenges by considering intrinsic hazards, indicator volatility and correlations, while overcoming subjectivity in weight assignment and the neglect of flame-retardant treatments. Firstly, we initiate two novel steps of Pearson correlation analysis and predefined comparative scales for indicators, which enhance the objectivity, scalability and repeatability of analytic network process (ANP) weights. Secondly, we utilize the CRITIC method to incorporate indicator volatility and correlations, surpassing the limitations of separate calculations and subsequent combinations. The newly developed pure sample normalization method enhances the interpretability and scientific validity of CRITIC weights. Combining the ANP and CRITIC weights using Nash equilibrium theory, the results rectify the underestimated T-5% from 0.72% to 3.64%, reducing the overestimated PCO2, PSP and TSP from 11.98%, 9.62% and 9.59–7.25%, 9.12% and 5.69%, respectively. Finally, the fire hazards value effectively captures the increased fire hazards resulting from specific degraded indicators following flame retardant treatments. The research findings provide a fresh perspective for the safe design of flame-retardant polymers and fire hazard assessment, offering valuable insights for fire risk prevention and management.