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ABSTRACT Accessibility between fire hydrants and buildings is paramount in emergency response, significantly influencing the efficiency and effectiveness of firefighting operations in the event of an incident. However, assessing this relationship within a geographic information system (GIS) framework presents challenges on two fronts. Obtaining the path avoiding building and parcel obstructions to hydrants is not trivial. Further, determining the furthest extent around a building exterior from hydrants is complicated, yet it is critically important given the spatial limitations of equipment reach. To assess furthest extent, an analytical framework is introduced based on the Euclidean shortest path problem. The proposed approach offers a comprehensive, automated GIS‐based methodology tailored to evaluate the dynamic relationship between hydrants and buildings. The developed methods are able to accurately and precisely identify the furthest point around a building structure from hydrant, facilitating risk assessment as well as fire code compliance. This enables a comprehensive evaluation of potential loss and structure vulnerability at property, street, neighborhood, and regional levels. [ABSTRACT FROM AUTHOR]

Wildfires have the potential to result in significant loss of life and property damage. Mountain wave flow events (Berg winds) are integrally linked to periods of enhanced fire danger. The basis for enhanced fire danger during periods of mountain winds has long been established, supported by evidence from a number of case studies. A fuzzy logic model, applied to hourly meteorological data from four different sites varying in elevation, allowed for an in-depth study of the effects of Berg winds on the microclimate and fire danger. Results indicate that night-time Berg wind events occur less frequently than daytime events, also having a significantly reduced influence on the microclimate. Daytime Berg wind events were shown to result in a significant change in the microclimate and fire danger across all sites. Night-time Berg wind events did not result in significant change to air temperature and relative humidity but when combined with significant changes to wind speed did result in significant increases in fire danger. The sensitivity of the Lowveld Fire Danger Index was also tested. The study emphasizes the need for boundary layer processes such as Berg winds to be taken into account when forecasting and monitoring fire danger. [ABSTRACT FROM AUTHOR]

Fuel moisture content (FMC) is important for the ignitability, behaviour and severity of wildfires. Understanding the drivers of FMC and its spatial and temporal variability can help us develop fuel moisture models and inform assessments of wildfire behaviour and danger. Here we present the first United Kingdom (UK) national-scale temperate FMC dataset of 8,057 samples of eighteen different fuel constituents collected across 58 sampling sites between 2021–2023. We sampled fuels across emerging fire-prone ecosystems in the UK across three studies: (1) UK-wide longer-term sampling characterising the spatio-temporal drivers of FMC; (2) landscape-scale measurement through the North Yorkshire Moors to investigate landscape-driven variability in FMC; (3) plot-scale intensive sampling in the West Midlands to quantify diurnal patterns and among-sampler variability in fuel measurements. This database addresses a global fuel moisture measurement gap within traditionally non-fire prone regions. The database will advance our understanding of temperate fuel moisture dynamics and forms a fundamental contribution towards the development of a fire danger rating system for traditionally non-fire prone regions such as the UK. [ABSTRACT FROM AUTHOR]

The main objective of this research is to establish a quantitative risk assessment method for fire spread in enclosed building scenarios. The enclosed building fire spread process is divided into three stages: fire fully developed in the fire compartment, failure of weak barrier, and combustibles ignited in the target compartment. The calculation method for fire spread is established, where the time for fire fully developed is calculated based on the t2 fire, the barrier failure time is calculated based on the finite difference method, and the combustible ignition time is calculated based on the zone model. The linear regression model is formulated to ensure computational efficiency for fire spread time prediction. The enclosed building fire spread quantitative risk assessment method is proposed based on the Probit model. The effectiveness of the risk assessment method is validated through the enclosed building fire spread experiment, and the method is applied to assess the risk of fire spread in an office. The results demonstrate that the method could quantitatively assess fire spread risk under different conditions with high computational efficiency and excellent versatility, and it could provide guidance for fire prevention, building fire design, and fire rescue. [ABSTRACT FROM AUTHOR]

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks. [ABSTRACT FROM AUTHOR]

Metallum Fire-Resistant paint, denoted as MFR henceforth, represents a cutting-edge insulating material with dual functionality as a fireproof solution, presenting substantial advantages in the realm of construction applications. This exposition derives its primary insights from the scholarly contributions documented in publications. The focal point of these investigations includes the assessment of fire hazards associated with polyethylene materials in building structures and the enhancement of mortars in high-temperature environments in tunnels. The purpose of this study is to evaluate the effectiveness of a modified cork-based coating (MFR) compared to traditional coatings in terms of corrosion protection, fire resistance, and thermal insulation properties in construction applications. This evaluation focuses on quantifying the efficacy of MFR by examining key properties, such as adhesion, the reduced thickness required for fire protection, thermal conductivity reduction, and corrosion resistance under extreme environmental conditions. MFR is highly effective in fire prevention for buildings and tunnels, withstanding temperatures over 1000 °C while maintaining structural integrity. A unique aspect of MFR is its use of cork shavings, a typically underutilized byproduct from wine-bottle-stopper production. This innovative not only amplifies MFR's fire-resistant attributes, but also introduces sustainability and judicious resource utilization into its manufacturing processes. [ABSTRACT FROM AUTHOR]

Due to the complexity of spill fire, predicting heat release rate (HRR) is a challenging aspect, therefore, identifying key contributors to uncertainty is essential to develop reliable models for fire risk assessment. We propose a framework that couples Artificial-Neural-Network (ANN) and Monte Carlo Uncertainty Quantification (UQ) to quantify the uncertainties of spill fire parameters on peak HRR variance. The ANN spill fire model, trained on experimental spill fire data, shows good agreement with measured HRR values. By coupling with Dakota for Monte Carlo uncertainty propagation, the framework identifies major contributors to peak HRR uncertainty for fixed and continuous unconfined spill fires. This is achieved by investigating two uncertainty sources: data scarcity and input parameters. UQ results indicate that the confidence intervals widen at points where data are scarcer. Sensitivity analysis shows that in the case of fixed quantity spills, the fuel amount and properties parameters contribute to 54.4% and 22.6% of peak HRR variance, respectively. For continuous spills, the discharge rate and fuel properties parameters account for 41.8% and 39.3% of peak HRR variance, respectively. This work can be utilized in developing more advanced predictive ANN models for spill fire scenarios, ultimately enhancing fire probabilistic safety analysis in nuclear power plants. [ABSTRACT FROM AUTHOR]

This study assessed the spatio-temporal variation of the degree of curing (DoC) in the Golden Gate Highlands National Park (GGHNP) from 2016 to 2020. The estimation of the degree of grass curing was carried out with the high spatial resolution fused remotely sensed data of MODIS and Sentinel-2 employing Index-then-Blend (IB) data fusion methods on Google Earth Engine (GEE) to compute Grassland Curing Index (GCI) using soil and vegetation moisture content indices-based grassland curing algorithm. Grassland curing indices were computed on Google Earth Engine and converted into Grassland Curing Maps (GCMs), which were then synthesized into fire danger maps. The study showed that all selected indices have the highest DoC in the month of September, with the minor time variation observed in MODIS and Sentinel 2-derived GCI estimated using Shortwave Infrared Water Stress (GCI_SIWSI) in the months of August and March, respectively. The analysis further revealed that GCI_SIWSI (44%) derived from Sentinel-2 data yielded the highest area of extremely high fire danger, followed by GCI estimated using Normalized Difference Moisture Index (GCI_NDMI) (25%) derived from fused data, MODIS-derived GCI_SIWSI and fused-derived GCI estimated using Global Vegetation Monitoring Index (GCI_GVMI) (both 16%). The GCI_SIWSI derived from Sentinel-2 data also showed that more than 95% of the entire landmass of the study area was within a high to extremely high fire danger zone, making the park's vegetation susceptible to fire. Among all the four selected indices correlated against fire point, only SIWSI revealed a positive relationship across MODIS, Sentinel-2 and Fused data. It is also evident from the study that GCMs derived from Fused data outperformed MODIS and Sentinel-2 with the highest R2 and F values of 0.65 and 380, respectively. These results indicate that GCI derived from fused remotely sensed data is a promising GCI for accurately assessing the DoC over mountainous grassland environments. The study paved the way for increasing the spatial resolution for estimating the DoC for fire and fuel management of parks and other fire agencies within mountainous grassland environments. [ABSTRACT FROM AUTHOR]

Fire and explosion hazards pose significant safety concerns in the processing and storage of biomass particles, warranting the safe utilization of these particles. This study employed scanning electron microscopy, thermogravimetric analysis, and cone calorimetry to investigate the thermal hazards and toxicity of raw biomass particles from four prevalent agricultural crops in China: rice, sorghum, corn, and reed. Among the samples, corn exhibited the highest heat output of 8006.82 J/g throughout the thermal decomposition process. The quantitative evaluation of critical heat flux, heat release rate intensity, fire growth rate index (FIGRA), post-ignition fire acceleration (PIFA) and flashover potential (X) revealed a substantial fire risk inherent to all the examined straw samples. Notably, corn displayed the lowest FIGRA value of 8.30 kW/m2 s, while rice demonstrated the minimum PIFA value of 16.11 kW/m2 s. Moreover, the X values for all four biomass particle types exceeded 10 under varying external heat flux levels, indicating their high propensity for fire hazards. Analysis of CO and CO2 emissions during combustion showed all four biomass samples exhibited high concentrations throughout, from the initial stages to the end. The present study offers crucial insights for formulating comprehensive fire safety guidelines tailored to the storage and processing of biomass particles. [ABSTRACT FROM AUTHOR]

The majority of fatalities in building fires are attributed to asphyxiation caused by toxic gases. Hydrogen cyanide (HCN) is one of the toxic gases that can be released during a fire, posing a lethal risk to humans even at low concentrations. However, analysis of the risk posed by HCN in fire risk assessments using fire simulations is relatively rare. This study conducted fire simulations to examine the potential risks of HCN to occupants during a fire. The simulations considered various fire conditions in residential buildings by varying fuel types, fire growth rates, and HCN yields. The relative risk score (RRS) was derived based on the time to reach the threshold values of parameters considered critical for life safety. The results of the fire simulations indicated that the RRS for HCN was approximately 20–40 points higher than that of O2, CO, and CO2, reaching a maximum of 92 points. However, the risk posed by HCN was found to be limited in comparison to the risks associated with temperature and visibility. Nevertheless, considering that the primary cause of fatalities in fires is asphyxiation due to toxic gases, HCN must be regarded as a critical factor in fire risk assessments. Additionally, since HCN yield values can increase up to nine times depending on temperature and ventilation conditions, the risk posed by HCN could be significantly higher. [ABSTRACT FROM AUTHOR]

The laboratory is a high-risk place for scientific research and learning, and there are many risk factors and great potential for harm. Hazardous chemicals are important to consider and are the key objects to monitor in a laboratory. In recent years, hazardous chemical fire accidents have occurred in laboratories in various industries, bringing painful lessons and making it urgent to strengthen the safety management of hazardous laboratory chemicals. In this study, a semi-quantitative comprehensive risk assessment model for hazardous chemical laboratory fires was constructed by combining the bowtie model, three-dimensional risk matrix, and analytic network process (ANP). This study applied this method to the management of hazardous chemicals at the TRT Research Institute; evaluated the probability, severity, and preventive components of the corresponding indicators by constructing different index systems; and calculated the evaluation results using the weight of each index. The evaluation results show that the comprehensive likelihood level is 2, the comprehensive severity level is 3, the comprehensive preventive level is 3, and the final calculated comprehensive risk level is tolerable (II). Based on the results of the risk assessment, the corresponding control measures that can reduce the fire risk of hazardous chemicals in the laboratory are proposed according to the actual situation at the TRT Research Institute. [ABSTRACT FROM AUTHOR]

In the Mediterranean region, particularly in Antalya, southern Türkiye, rising forest fire risks due to climate change threaten ecosystems, property, and lives. Reduced soil moisture during the growing season is a key factor increasing fire risk by stressing plants and lowering fuel moisture content. This study assessed soil moisture and fuel moisture content (FMC) in ten fires (2019–2021) affecting over 50 hectares. The Fire Weather Index (FWI) and its components (FFMC, DMC, DC) were calculated using data from the General Directorate of Meteorology, EFFIS (8 km), and ERA5 (≈28 km) satellite sources. Relationships between FMCs, satellite-based soil moisture datasets (SMAP, SMOS), and land surface temperature (LST) data (MODIS, Landsat 8) were analyzed. Strong correlations were found between FWI codes and satellite soil moisture, particularly with SMAP. Positive correlations were observed between LST and FWIs, while negative correlations were evident with soil moisture. Statistical models integrating in situ soil moisture and EFFIS FWI (R: −0.86, −0.84, −0.83 for FFMC, DMC, DC) predicted soil moisture levels during extended fire events effectively, with model accuracy assessed through RMSE (0.60–3.64%). The SMAP surface (0–5 cm) dataset yielded a lower RMSE of 0.60–2.08%, aligning with its higher correlation. This study underlines the critical role of soil moisture in comprehensive fire risk assessments and highlights the necessity of incorporating modeled soil moisture data in fire management strategies, particularly in regions lacking comprehensive in situ monitoring. [ABSTRACT FROM AUTHOR]

The use of fireworks as a sign of celebration is a common practice in many countries. During the last few years, this sector has been subjected to various social criticisms that have even led to a ban on their use in different places. One of the main causes is the environmental pollution they generate, due to emissions from combustion reactions associated with the use of pyrotechnic devices. Nitrate ester-based compounds as propellants are used in other solid rockets such as ammunition, but their use in pyrotechnics is currently limited to a small number of fireworks. This research analyses the substitution of black powder with nitrocellulose ([C6H7(NO2)3O5]n) as a propellant for aerial articles, as well as to the use of nitrocellulose as a perchlorate-free compound. In addition to achieving better clarity effects due to less smoke, the use of nitrocellulose reduces greenhouse gas emissions. However, this nitrogen-rich compound is more unstable, and the ignition sensitivity is higher. Therefore, the benefits and risks associated with this alternative compound need to be evaluated, but the evidence shown suggests a promising direction for the sustainable evolution of pyrotechnic compositions. [ABSTRACT FROM AUTHOR]

The increasing number of fires in semiconductor factories requires new approaches to fire safety. It is important to study the specifics of the activities of companies that use potentially flammable materials in production, such as air filtration units, electrical cables and floor panels. The aim of the study was therefore to determine the level of fire risk in the clean rooms of these companies by means of real fire experiments. As a result, a fire risk assessment of the main combustible materials such as air filtration units, electrical cables and floor panels in the plenum room on the top floor of the cleanroom was carried out. The results of the experiment showed a low ignition propensity of the air filtration unit and limited fire propagation in the event of ignition. High calorific materials, such as fibreglass in filters, were identified as increasing the risk. Based on this, it was proposed to replace these materials with flame retardant materials and to improve the stop/fire control systems of the air filtration units. The results obtained in the study should be used for the development of technical recommendations for improving fire safety in critical premises at semiconductor factories. [ABSTRACT FROM AUTHOR]

Global healthcare fires cause major casualties. Despite fewer incidents in Thailand, fire prevention is crucial. This study develops a prototype of fire risk assessment model for hospital buildings in urban areas aligned with NFPA 551 and OHSAS 18001, The model comprised four steps: 1) Hazard identification and fire opportunity assessments (Sources of fire, people at risk, fire and life safety and fire protection management); 2) Fire severity assessment; 3) Fire risk level assessment and 4) Fire risk management of hospital buildings in urban areas. Data was collected through interviews with eight specialists and by testing the model with simulated scenarios by eighteen specialists. Results indicated that fire opportunities in Category 4 had the highest risk score at 64.14%, followed by Category 1 at 62.58%, Category 3 at 58%, and Category 2 at 52.53%. Calculating opportunities using an equation yielded a value of 59.85, categorizing it as "Moderate". Fire severity was assessed as "High", implying a severe impact on building occupants, potentially leading to disability or death, and significant structural damaged. A "High" fire risk level mandates proactive safety management to mitigate risks before operations commence. [ABSTRACT FROM AUTHOR]

In recent decades, the increasing frequency of urban fires, driven by urban functional enhancements and climate change, has posed a growing threat to metropolitan sustainability. This study investigates the temporal and spatial characteristics of fire incidents in Shanghai from 2019 to 2023. Using satellite fire point data and official government records, kernel density analysis and wavelet analysis were employed to analyze the time series and spatial distribution of fire data. Subsequently, eleven primary factors influencing urban fire occurrence were identified, encompassing probability, regional characteristics, and hazard sources. A combined methodology of subjective and objective weights with game theory was used to generate a fire risk assessment at a 1 × 1 km2 grid scale. Furthermore, the spatial distribution characteristics of the assessments were analyzed. The results reveal that the downtown area exhibits the highest intensity of urban fires in terms of spatial domain, with a decreasing intensity towards the suburbs. Temporally, fire frequency demonstrates significant periodicity at an 18a time scale, while clear seasonal fluctuations and periodicity are observed at a 16-22a time scale, with higher occurrences in spring and winter. The study identifies typical aggregation patterns of urban fires, with high-risk centers in downtown Shanghai. Considering the impact of climate change and human activities, high-risk areas may gradually expand to adjacent urban suburbs, presenting a concerning future scenario. By examining the dual attributes of "combustibles and fireproof space" within urban greening systems, this research offers recommendations for the future strategies of disaster prevention and mitigation of green systems in Shanghai. [ABSTRACT FROM AUTHOR]

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Commercial buildings are considered as high‐risk units in fire due to the existence of expensive equipment and large presence of people with different ages. Therefore, the present study aimed to assess the fire risk of commercial and shopping centers in Kashan using the fire risk assessment method for engineering (FRAME). This cross‐sectional study was conducted using FRAME in 15 commercial and shopping centers in Kashan, Iran. The risk assessment data and FRAME checklist were collected by observation and interviews. Data analysis was conducted using Excel and Geographic Information System (GIS) software. The results of 46 checklists showed that the mean score of fire risk R for property was equal to 3.12, for people 3.81 and for activities 1.86. Also, the lowest risk for the property, people and activities belongs to Golestan Center with a score of 1.40, Valiasr with a score of 1.25 and Ataee with a score of 1.15, and the highest risk for the property, people and activities belongs to Mir Seyfi Center with a score of 5.80, Shafagh with a score of 9.96 and Sabz was 2.62, respectively. The present study showed the lack of proper ventilation system, lack of emergency exits, and lack of automatic fire alarm and extinguishing system were the factors affecting fire risk. Therefore, in order to prevent disasters and uncontrollable critical conditions, it is necessary to review the equipment of these buildings and to design and implement fire detection and alarm equipment and different ways of fire control in these centers. [ABSTRACT FROM AUTHOR]

The Canadian Fire Weather Index System (CFWIS), empirically developed for forests in Canada, estimates the fuel moisture content ( m f ) at different depths and loads through meteorological parameters. While it is often suggested that adapting an existing fire danger rating system like CFWIS for a new environment requires developing new relationships or modifying existing ones, it is worth considering if such adaptations are always necessary. Based on a dataset of field measurements for surface litter (Pinus pinaster) carried out in the central region of Portugal (2014–2023), we propose a correction of m f based on the Fine Fuel Moisture Code (FFMC) of the CFWIS. This moisture correction was used to determine the Initial Spread Index (ISI) directly and, subsequently, the Fire Weather Index (FWI). Fire records from the study region were used to analyze the performance of the corrected indices. We found that the moisture correction led to higher values and potentially more accurate indices under dry conditions but did not provide a significant improvement in predicting the number of fires and burned areas compared to the original indices. The results suggest that, in relation to fire activity, the CFWIS is sufficiently robust to variations in the fuel moisture content in the study region. [ABSTRACT FROM AUTHOR]

Digital technologies can support designers in balancing architectural heritage conservation and performances using multidisciplinary approaches. Fire safety represents a challenging issue, especially in public historical buildings hosting vulnerable occupants, since heavy modifications are often required to facilitate their evacuation. Digital tools based on evacuation simulation are able to verify the impact of other sustainable, compatible evacuation management and planning approaches, especially considering the use of generic software, which can be used by low-trained technicians according to rapid setups. Nevertheless, simulator reliability should be experimentally verified through case study applications. This work thus offers the experimental verification of a rapid setup-based generic evacuation simulator in the context of a significant case study (the "Omero Museum", Ancona, Italy), placed in a historic building hosting vulnerable occupants (disabled, elderly, children), thanks to a full-scale evacuation drill. The rapid setup described different vulnerable occupants' categories according to literature data. Comparisons between drill and simulation results, using consolidated verification indicators, showed the overall reliability of the proposed approach, and thus encourage additional tests in historical buildings. The proposed setup-based simulator could be combined with other digital tools (virtual reality, BIM-related) to provide full support to fire risk and evacuation assessments when vulnerable occupants are present. [ABSTRACT FROM AUTHOR]

Algeria, the main fire hotspot on the southern rim of the Mediterranean Basin, lacks a complete fire dataset with official fire perimeters, and the existing one contains inconsistencies. Preprocessed global and regional burned area (BA) products provide valuable insights into fire patterns, characteristics, and dynamics over time and space, and into their impact on climate change. Nevertheless, they exhibit certain limitations linked with their inherent spatio-temporal resolutions as well as temporal and geographical coverage. To address the need for reliable BA information in Algeria, we systematically reconstructed, validated, and analyzed a 40-year (1984–2023) BA product (NEALGEBA; North Eastern ALGeria Burned Area) at 30 m spatial resolution in the typical Mediterranean ecosystems of this region, following international standards. We used Landsat data and the BA Mapping Tools (BAMTs) in the Google Earth Engine (GEE) to map BAs. The spatial validation of NEALGEBA, performed for 2017 and 2021 using independent 10 m spatial resolution Sentinel-2 reference data, showed overall accuracies > 98.10%; commission and omission errors < 8.20%; Dice coefficients > 91.90%; and relative biases < 3.44%. The temporal validation, however, using MODIS and VIIRS active fire hotspots, emphasized the limitation of Landsat-based BA products in temporal fire reporting accuracy terms. The intercomparison with five readily available BA products for 2017, by using the same validation process, demonstrated the overall outperformance of NEALGEBA. Furthermore, our BA product exhibited the highest correspondence with the ground-based BA estimates. NEALGEBA currently represents the most continuous and reliable time series of BA history at fine spatial resolution for NE Algeria, offering a significant contribution to further national and international fire hazard and impact assessments and acts as a reference dataset for contextualizing future weather extremes, such as the 2023 exceptional heat wave, which we show not to have led to the most extreme fire year over the last four decades. [ABSTRACT FROM AUTHOR]

Fire statistics indicate that fire, despite significant technical progress in the field of fire protection, is a significant risk to the long-term preservation of cultural property. Management of the risk requires tools which quantitatively relate the risk of fire to the characteristics of buildings and collections and the effectiveness of fire protection measures. Such tools must be accessible to, and useful for, conservation professionals and related decision-makers. To address the challenge, a module assessing fire risk for collections in museums has been developed on the HERIe online digital decision-supporting platform using the methodology proposed by Jean Tétreault transformed into a mathematical algorithm. It takes into account building characteristics and the proportion of the collection stored in showcases, as well as the vulnerability of the collection materials to heat, combustion, smoke, and water. This information is used to forecast loss to collection value over a selected time horizon. Fire likelihood is estimated from the fire statistics available in the user’s country, area, or organization. Risks to collections associated with correct and unwanted activation of automatic fire suppression systems are also considered. [ABSTRACT FROM AUTHOR]

Risk-based design and assessment methods are gaining popularity in performance-based structural fire engineering. These methods usually start by defining a set of hazard scenarios to use as analysis inputs. This approach, proven highly effective for other hazard types such as earthquakes, may not be optimal for fire safety design. Indeed, the strong coupling between the fire phenomenon and structural features enables an ad-hoc design variable selection (and/or optimisation) to reduce fire intensity, making fire scenarios additional design outputs. In addition, such a coupling effect implies that fire scenarios maximising consequences are structure specific. Building on these considerations, this paper discusses the limitations that arise at different analysis steps (i.e., fire-scenario and intensity treatment, identifying fire intensity measures, probabilistic fire hazard analysis, developing fire fragility models, and risk calculation) when using conventional risk-based approaches for design purposes. Furthermore, it compares such approaches with a fire safety design methodology (the Consequence-oriented Fire intensity Optimisation, CFO, approach) that addresses the identified limitations. The potential benefits of integrating the two approaches are also discussed. Finally, the fire design of a simplified steel-girder bridge is introduced as an illustrative example, comparing the consequence metrics and design updating strategies resulting from the two approaches. [ABSTRACT FROM AUTHOR]

Protecting forest resources and preventing forest fires are vital for social development and public well-being. However, current research studies on forest fire warning systems often focus on extensive geographic areas like states, counties, and provinces. This approach lacks the precision and detail needed for predicting fires in smaller regions. To address this gap, we propose a Transformer-based time series forecasting model aimed at improving the accuracy of forest fire predictions in smaller areas. Our study focuses on Quanzhou County, Guilin City, Guangxi Province, China. We utilized time series data from 2021 to 2022, along with remote sensing images and ArcGIS technology, to identify various factors influencing forest fires in this region. We established a time series dataset containing twelve influencing factors, each labeled with forest fire occurrences. By integrating these data with the Transformer model, we generated forest fire danger level prediction maps for Quanzhou County. Our model's performance is compared with other deep learning methods using metrics such as RMSE, and the results reveal that the proposed Transformer model achieves higher accuracy (ACC = 0.903, MAPE = 0.259, MAE = 0.053, RMSE = 0.389). This study demonstrates that the Transformer model effectively takes advantage of spatial background information and the periodicity of forest fire factors, significantly enhancing predictive accuracy. [ABSTRACT FROM AUTHOR]

Wildfire is one of the primary natural disturbance agents in the forests of China. The forecast of fire danger is critically important to assist stakeholders to avoid and mitigate wildfire-induced hazards and losses to both human society and natural ecosystems. Currently, fire danger rating methods often focus on fire weather classification based on fixed thresholds, which has shortcomings in generalizability and robustness. Based on historical fire occurrence data and meteorological data of Northeastern China from 2004 to 2015, we proposed a forest fire danger rating classification and forecasting model by combining the advantages of the Canadian Fire Weather Index (FWI) system and two machine learning models such as the Long Short-Term Memory (LSTM) network and Random Forest (RF) model. The method is divided into two stages. The first stage is the LSTM-based FWI system indexes prediction. In the first stage, the future FWI system indexes are obtained through the LSTM-based prediction model, and the RMSE and MAE of the prediction results are calculated to verify the prediction performance of the model. The second stage is random forest-based fire danger rating prediction method. In the second stage, we use the random forest method to get the fire danger occurrence probability and present the fire danger rating classification scheme. Then we verify the reliability of the fire danger rating classification scheme by using the forest fire danger data in Qipan Mountain. Our method predicts two randomly selected future intervals, and the prediction accuracy is 87.5%. The experimental results show that our machine learning-based forest fire danger rating classification method can provide a new idea for forest fire danger warnings. [ABSTRACT FROM AUTHOR]

Abstract Fire and explosion hazards pose significant safety concerns in the processing and storage of biomass particles, warranting the safe utilization of these particles. This study employed scanning electron microscopy, thermogravimetric analysis, and cone calorimetry to investigate the thermal hazards and toxicity of raw biomass particles from four prevalent agricultural crops in China: rice, sorghum, corn, and reed. Among the samples, corn exhibited the highest heat output of 8006.82 J/g throughout the thermal decomposition process. The quantitative evaluation of critical heat flux, heat release rate intensity, fire growth rate index (FIGRA), post-ignition fire acceleration (PIFA) and flashover potential (X) revealed a substantial fire risk inherent to all the examined straw samples. Notably, corn displayed the lowest FIGRA value of 8.30 kW/m2 s, while rice demonstrated the minimum PIFA value of 16.11 kW/m2 s. Moreover, the X values for all four biomass particle types exceeded 10 under varying external heat flux levels, indicating their high propensity for fire hazards. Analysis of CO and CO2 emissions during combustion showed all four biomass samples exhibited high concentrations throughout, from the initial stages to the end. The present study offers crucial insights for formulating comprehensive fire safety guidelines tailored to the storage and processing of biomass particles.

Accurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data‐driven machine learning approach to construct a global daily ∼9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence. Plain Language Summary: Wildfires have widespread effects on local ecosystems, communities, air quality, and global atmospheric conditions. Accurate wildfire forecasts can be used by local communities and agencies to manage and respond to wildfires effectively. As such, it is essential these predictions are not only accurate but are accessible in real‐time and provide sufficient advanced notice to ensure successful actions can be taken. To achieve this we have developed a forecasting system that combines satellite observations, weather forecasting, and vegetation characteristics using machine learning. Tested on historical data and operated in real‐time, our model provides a global daily wildfire forecast with a 9 km resolution, predicting the likelihood of fires up to 30 days in advance. The model outperforms existing fire danger forecasts when evaluated against satellite observations of active fires. It can also identify key drivers which result in the occurrence of fire. Finally, it not only offers real‐time forecasts but can be used to help investigate past fire events, understand their causes, and predict wildfire activity over longer climate timescales. Key Points: A data‐driven model informed by satellite observations and an Earth System Model provides accurate fire forecasts upto 10 days in advanceThe probability of fire forecast is implemented operationally in a numerical weather prediction model to provide real‐time forecastsFire attribution is demonstrated and can be used for specific fire events or historical analysis [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wildfires cause great losses to the ecological environment, economy, and people's safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. [ABSTRACT FROM AUTHOR]

Wildfire agencies use fire danger rating systems (FDRSs) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian fire weather index (FWI) system, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly fire radiative power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI's ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system's value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior. [ABSTRACT FROM AUTHOR]

Wildfires pose a severe threat to the ecosystem and economy, and risk assessment is typically based on fire danger indices such as the McArthur Forest Fire Danger Index (FFDI) used in Australia. Studying the joint tail dependence structure of high-resolution spatial FFDI data is thus crucial for estimating current and future extreme wildfire risk. However, existing likelihood-based inference approaches are computationally prohibitive in high dimensions due to the need to censor observations in the bulk of the distribution. To address this, we construct models for spatial FFDI extremes by leveraging the sparse conditional independence structure of Hüsler–Reiss-type generalized Pareto processes defined on trees. These models allow for a simplified likelihood function that is computationally efficient. Our framework involves a mixture of tree-based multivariate generalized Pareto distributions with randomly generated tree structures, resulting in a flexible model that can capture nonstationary spatial dependence structures. We fit the model to summer FFDI data from different spatial clusters in Mainland Australia and 14 decadal windows between 1999 and 2022 to study local spatiotemporal variability with respect to the magnitude and extent of extreme wildfires. Our proposed method fits the margins and spatial tail dependence structure adequately and is helpful in providing extreme wildfire risk estimates. Our results identify a significant increase in spatially aggregated fire risk across a substantially large portion of Mainland Australia, which raises serious climatic concerns. Supplementary material to this paper is provided online. [ABSTRACT FROM AUTHOR]

Fire frequencies have increased in the natural ecosystems of northeastern Iran because of natural and human factors in the recent years. This study models and maps the danger of fire in natural ecosystems in Northern Khorasan Province in northeastern Iran. First, the hierarchical structure of the main fire-influencing factors (physiographic, climatic, vegetative, and human-made factors) and their sub-factors was determined. The data describing these effective factors were acquired from different sources (satellite images, field survey, etc.) and were converted into the maps in raster format (30 m by 30 m pixel size) in the GIS. A map of past fires in the province was developed from data of the Northern Khorasan Natural Resources Administration and from the fire product of the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. The weights of each of the factors and sub-factors were determined using FAHP (Fuzzy Analytic Hierarchy Process). A fire-danger map was obtained by overlapping all effective-factor maps in the GIS. The raster values were categorized into five fire-danger classes. The results were validated by comparison to the past fires using OA (Overall Accuracy), and kappa index. Results demonstrated that the human-influence factor, with weight of 0.48, is the most effective variable in fire-danger modeling in the Northern Khorasan Province. The fire-danger map indicated that very high-danger and high-danger areas cover 54.65% of the province, a spatially extensive amount of land having high fire likelihood. In statistical terms, the accuracy of fire danger map was high with OA of 96%, and the kappa index of 0.85, reflects the high accuracy of fire-danger prediction in the study area. The fire danger map of the study area provides a baseline for informed fire management, mitigation, and prevention decisions in the natural ecosystems of Northern Khorasan Province. This map could be very useful for forecasting future fires in northeastern Iran. [ABSTRACT FROM AUTHOR]

Due to the increased number of forest fires, a detailed examination and knowledge of the effects of the microclimatic conditions of forests is currently significant. The study carried out in Arboretum Borova hora (Slovak Republik), investigates the influence of meteorological factors (air temperature, relative humidity), the value of the Angström index, and the danger class of the Angström index on the moisture content of fine fuel at the edge of a beech forest stand, but also in its interior. We tested three working hypotheses: a) meteorological conditions differ significantly between the edge and the interior of the beech forest, b) the moisture content of fine fuel is higher in the beech forest interior than at the forest edge, c) the Angström index fire danger class is higher at the edge of the beech forest than in its interior. We created a calibration curve that was also used to measure the humidity of beech leaves with the help of the ME 2000 hygrometer. Our results show that edge beech stands are significantly more susceptible to fires, lower air humidity and fine fuel moisture content, and higher air temperatures than forest interiors. From our point of view, the microclimate is considered the main factor that explains the difference between the vegetation structure of the forest edge and the forest interior. [ABSTRACT FROM AUTHOR]

Forest fires are sudden and difficult to extinguish, so early risk assessment is crucial. However, there are currently a lack of suitable knowledge-mining algorithms for forest fire risk assessment. This article proposes an improved continuous Apriori algorithm to mining forest fire rules by introducing prior knowledge to classify input data and enhance its ability to process continuous data. Meanwhile, it constructs an ontology to provide a standardized expression platform for forest fire risk assessment. The improved continuous Apriori algorithm cooperates with ontology and applies the mining rules to the forest fire risk assessment results. The proposed method is validated using the forest fire data from the Bejaia region in Algeria. The results show that the improved continuous Apriori algorithm is superior to the raw Apriori algorithm and can mine the rules ignored by the raw Apriori algorithm. Compared to the raw Apriori algorithm, the number of generated rules increased by 191.67%. The method presented here can be used to enhance forest fire risk assessments and contribute to the generation and sharing of forest-fire-related knowledge, thereby alleviating the problem of insufficient knowledge in forest fire risk assessment. [ABSTRACT FROM AUTHOR]

This paper aims to automatize the performance-based design of fire engineering and the fire risk assessment of buildings with large open spaces and complex shapes. We first establish a database of high-quality fire simulations for diverse building shapes with heights up to 60 m and complex atriums with volumes up to 22   400 m³. Then, artificial intelligence (AI) models are trained to predict the soot visibility slices for new fire cases in buildings of different atrium shapes, symmetricities, and volumes. Two deep learning models were demonstrated: the pix2pix generative adversarial network (GAN) and image-prompt diffusion model. Compared with high-fidelity computational fluid dynamics fire modeling, the available safe egress time predicted by both models shows a high accuracy of 92% for random atrium shapes that are not distinct from the training cases, proving their performance in actual design practices. The diffusion model reproduces more flow details of the smoke visibility profiles than GAN, but it takes a longer computational time to render the fire scene. This work demonstrates the potential of leveraging AI technologies in building fire safety design, offering significant cost and time reductions and optimal solution identification. [ABSTRACT FROM AUTHOR]

Smoke control technology is crucial in the risk management of fire in underground confined spaces. In this study, the integrated foam smoke reduction device (FSRD) of "foaming-spraying" is proposed in response to the challenging problem of smoke control of fire in underground confined spaces. The overall structure of FSRD device is designed and optimized through the numerical simulation of the internal flow field and experimental research. The optimal basic parameters are as follows: area ratio (Rm) of 20.25, pitch-to-diameter ratio (Rtd) of 1/3 or 5/9, and throat diameter (d3) of 18 mm. Additionally, the throat-nozzle distance (Lt) is 12 mm, and the operating pressure (p0) is 3.0 MPa. Finally, the smoke reduction experiment verified that the FSRD designed in this study had a better effect of elimination than fine water mist and cylindrical foam jet, with the elimination efficiency reaching 36.73%. This study provides a new device for smoke reduction in controlling fires in underground confined spaces. [ABSTRACT FROM AUTHOR]

In the future, hydrogen will be more frequently injected into natural gas networks for storage and transportation purposes. A diffusion jet flame forms when gaseous fuel leakage occurs during the transportation or utilization of hydrogen-enriched natural gas, and the fuel is ignited by an ignition source in still air. In the study, methane was chosen to simulate natural gas. Experiments with the hydrogen addition fraction reaching up to 20% were performed to study the effects of the initial flame angle (θ = 90°, 60°, 30°, 0°, and −30°), variable nozzle diameter (d e = 3.15, 4, 4.94, 5.65, and 6 mm) and a multitude of fuel flow rates (10, 20, 30, 40, 50, and 60 L/min) on the morphological parameters of the gas jet flames. The geometrical features of the jet flame were determined via video processing for each experiment. The geometrical parameters of hydrogen-enriched natural gas were quantified under different conditions. The experimental results indicate that a dimensionless prediction model was established for the flame height of hydrogen-enriched natural gas by modifying the flame Froude number ( F r f) for vertical jet flames. For upward jet flames, the normalized flame horizontal projection length (L x) of hydrogen-enriched natural gas is well correlated with Q ∗. A global prediction model for inclination angles of 0° ≤ θ ≤ 60° and 2000 < Q ∗<20,000 was proposed based on the experimental data. For downward jet flames, the normalized horizontal projection length and downward extension length (L d) of the hydrogen-enriched natural gas jet flame are correlated with Q ∗, with powers of 2/5 and 3/5, respectively. Dimensionless prediction models for the horizontal projection length and downward extension length are given by modifying the flame Froude number. Quantitative results were proposed in order to gain insight into the development of the key parameters and the effects on the environment, from which extinguishing concepts and safety areas can be directly derived. • Hydrogen-enriched natural gas jet fire under various conditions was studied. • Macroscopic characteristics of hydrogen-enriched natural gas jet fire were quantified. • A unified empirical model was established to predict the horizontal extension length of hydrogen-enriched natural gas jet fire. • The normalized flame height and downward extension length correlated to the modified Froude number. [ABSTRACT FROM AUTHOR]

Introduction: Understory removal is a traditional practice in forest management to reduce fire risk and promote seedling regeneration. However, its effect on understory diversity, biomass and soil nutrients in temperate forest ecosystems is less known, which limits our assessment of the effectiveness of understory vegetation management. Methods: We quantified the composition of the understory species, their diversity, and the biomass of the understory and factors driving changes in these parameters in primary mixed broad-leaved Pinus koraiensis forest (BKF), secondary Betula platyphylla forest (BF), and Larix gmelinii plantation (LF) in northeast China after a 5-year understory removal. Results: After understory removal, the number of shrub and herb species in BKF and LF decreased, while the number of shrub species in BF increased significantly and that of herb species decreased; the species with strong light preference, Equisetum hyemale, Impatiens noli-tangere, and Filipendula Palmata, were dominant in the herb layer of the three forest types; Shannon-Wiener diversity, Pielou evenness, and Simpson diversity of the herb layer in LF increased significantly (P < 0.05), while those of the shrub and herb layers in BF and LF showed no significant changes (P > 0.05). The total understory biomass of understory of BKF and LF decreased by 0.94 t·hm-2 and 1.32 t·hm-2, respectively, while that of BF increased by 1.31 t·hm-2; soil NH+ 4 -N and total phosphorus (TP) were the key factors regulating understory vegetation diversity and biomass, respectively. Conclusion: These results suggest that understory removal is a beneficial management strategy for increasing shrub biomass and diversity in secondary forests, while it should be avoided in primary forests and plantations to prevent the reduction of understory plant diversity and soil nutrient loss. [ABSTRACT FROM AUTHOR]

Fire risk analysis of electric vehicles and internal combustion vehicles in open car parks: part 2: fire safety and reliability analysis In order to counteract climate change, it is necessary to switch from vehicles with combustion engines to electric vehicles. Consequently, an increasing number of electric vehicles can be observed in Germany. In this context, the question arises whether and if how this development influences the fire risks in open car parks. Answers to this question are the subject of a research project under the leadership of the Institute of Steel Construction of Leibniz University Hannover in cooperation with the iBMB, Division of Fire Safety, of TU Braunschweig. In particular, the project is investigating the influence of a fire scenarios with electric vehicles compared to vehicles with combustion engines on the load‐bearing behaviour structure of an open car park. In this paper, the methodological approach to evaluate the fire risk based on a reliability analysis for the load‐bearing structure of these car parks is explained. By means of the reliability analysis, the fire risk assessment procedure is illustrated for the composite beam using exemplary fire scenarios. As a result, it can be concluded from the fire risk analysis that fire scenarios with electric vehicles do not have a higher fire risk than conventional vehicles with combustion engines and fulfil the required safety level. [ABSTRACT FROM AUTHOR]

Background: Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting. Aims: In this study, we assess the impact of synoptic-scale upper-air patterns on the occurrence of large wildfires and widespread fire outbreaks in the US Pacific Northwest. Additionally, we examine how discrete upper-air map types can augment subregional models of wildfire risk. Methods: We assess the statistical relationship between synoptic map types, surface weather and wildfire occurrence. Additionally, we compare subregional fire danger models to identify the predictive value contributed by upper-air map types. Key results: We find that these map types explain variation in wildfire occurrence not captured by fire danger indices based on surface weather alone, with specific map types associated with significantly higher expected daily ignition counts in half of the subregions. Conclusions: We observe that incorporating upper-air map types enhances the explanatory power of subregional fire danger models. Implications: Our approach provides value to operational wildfire management and provides a template for how these methods may be implemented in other regions. This study assesses the predictive value of synoptic-scale upper-air map types in near-term fire weather forecasting in the US Pacific Northwest. A set of 13 patterns of 500-hPa geopotential heights are associated with spatially heterogeneous variation in wildfire activity within the region. [ABSTRACT FROM AUTHOR]

CILRT is an important work in the overhaul of nuclear power plants. VOCs from paints and cleaners used in the containment during the overhaul will migrate inside the containment, and the numerous rooms and compartments inside the containment make the air flow impeded, and the VOCs may be enriched to bring combustion risk. The complete fire risk analysis method includes volatilization kinetics of chemicals, mixture explosion limits, and numerical simulation of VOCs diffusion and migration. The volatilization rate at the lowest room temperature of 20°C is used as a conservative input, the complement of CAITA realistic model is used as the flow domain, and the geometry is appropriately simplified, and the steady ventilation process, the internal flow and the laws of diffusion and agglomeration of 12 kinds of VOCs gases in the 9-h pressure-holding process are investigated by Computational Fluid Dynamics. The simulation results of steady ventilation show that the VOCs gases are almost uniformly carried by the air to all spaces inside the containment, and the explosion risk is low. The concentration field of the steady ventilation process was taken as the initial boundary condition and unsteady state simulation of the 9-h holding pressure process was performed. The results show that the gas flow inside the containment basically stops after 3456 s of pressure holding. With the extension of time, the VOCs gases accumulate at the bottoms of the three lower layers of the containment under the effect of laminar diffusion and gravitational settling, where the total concentration of the mixed gases reaches the highest at the bottom of the lowest layer. The total concentration of the VOCs gas mixture was below the lower explosion limit throughout the 9-h pressure-holding process, and the explosion risk was low. [ABSTRACT FROM AUTHOR]

Considering that the determination of fire hazard zones in warehouses is not sufficiently researched and studied, this paper aims to present a new methodological approach concerning the mentioned issue. Based on the COPRAS multi-criteria decision-making method, a new method was developed for the precise determination of potential zones where there is a risk of fire. The advantage of the described method is that it allows the quick and easy determination of all-orientation fire risk zones. The method requires fewer hardware resources compared to the existing ones and enables the display of the warehouse space in the form of a 3D model with calculated fire hazard zones. The mentioned procedure represents the first step when planning the layout and arrangement in the warehouse itself. The effectiveness of the proposed method was confirmed through a suitable numerical example. [ABSTRACT FROM AUTHOR]

With the development of image processing technology and video analysis technology, forest fire monitoring technology based on video recognition is more and more important in the field of forest fire prevention and control. The objects currently applied to forest fire video image monitoring system monitoring are mainly flames and smoke. This paper proposes a forest fire risk monitoring and early warning algorithm, which integrates a deep learning model, infrared monitoring and early warning, and forest fire weather index. The algorithm first obtains the current visible image and infrared image of the same forest area, utilizing a smoke detection model based on deep learning to detect smoke in the visible image, and obtains the confidence level of the occurrence of fire in said visible image. Then, it determines whether the local temperature value of said infrared image exceeds a preset warning value, and obtains a judgment result based on the infrared image. It calculates again a current FWI based on environmental data, and determines a current fire danger level based on the current FWI. Finally, it determines whether or not to carry out a fire warning based on said fire danger level, said confidence level of the occurrence of fire in said visible image, and said judgment result based on the infrared image. The experimental results show that the accuracy of the algorithm in this paper reaches 94.12%, precision is 96.1%, recall is 93.67, and F1-score is 94.87. The algorithm in this paper can improve the accuracy of smoke identification at the early stage of forest fire danger occurrence, especially by excluding the interference caused by clouds, fog, dust, and so on, thus improving the fire danger warning accuracy. [ABSTRACT FROM AUTHOR]

The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]