Fire Risk Research Articles

Accuracy assessment of LAI, PAI and FCOVER from Sentinel-2 and GEDI for monitoring forests and their disturbance in Central Germany

ABSTRACT Forest monitoring benefits from biophysical parameters such as Leaf Area Index (LAI), Plant Area Index (PAI) and fractional vegetation cover (FCOVER) and can be obtained from optical and LiDAR remote sensing, such as Sentinel-2 (S2) and the Global Ecosystem Dynamics Investigation (GEDI). While GEDI-derived products consider all phyto-elements, those from S2 refer to green elements only. Apart from individual accuracies, systemic deviations among products are thus expectable. However, products from S2 and GEDI lack inter-comparison. We evaluated S2 and GEDI-derived LAI, PAI and FCOVER with digital hemispherical photography observations (DHP) in a forest disturbance hotspot in Germany across various forest conditions, including vital stands, standing deadwood and clearings. We found moderate to high agreement with in-situ data, with highest accuracy for S2-derived LAI (R2 = 0.54) and GEDI-derived FCOVER (R2 = 0.73). Agreements between S2 and GEDI products were low, which we attribute to systematic influences of woody components, GEDI’s limitations in sloped terrain, and saturation of optical signals in dense canopy. In conclusion, findings suggest that while GEDI is effective in dense canopies, S2 products are beneficial for monitoring forest recovery. We also see potential for synergistic use in monitoring standing deadwood for habitat mapping and fire risk assessment.

Spatiotemporal pattern of ENSO-induced modulation on landscape fires over Pacific Rim from 2001‒2020

While El Niño-Southern Oscillation (ENSO) modulates landscape fire activities across the Pacific Rim, the detailed patterns and mode-specific variations of these effects remain poorly understood. This study aims to address that gap by systematically assessing the spatiotemporal variations in burned areas from 2001‒2020 during different modes of ENSO, namely, Eastern Pacific (EP) El Niño, EP La Niña, Central Pacific (CP) El Niño and CP La Niña. We evaluate the modulation patterns using classical correlation and cross-spectrum analyses, focusing on five hotspot regions: Temperate North America (TENA), Southern Hemisphere South America (SHSA), Northern Hemisphere South America (NHSA), Equatorial Asia (EQAS) and Australia and New Zealand (AUST). El Niño and La Niña exhibit asymmetrical opposite effects on burned areas, as expected. The findings reveal spatial non-uniformity in the impact of ENSO on landscape fires, with La Niña enhancing burned areas in TENA and SHSA, whereas El Niño predominantly affects AUST, EQAS and NHSA. CP and EP ENSO events differ in intensity and their large-scale circulation patterns. These differences cause notable variations in the regional burned area. Water vapour redistribution emerges as the key driver. The study emphasises the heterogeneity in ENSO modes in regulating landscape fires, providing insights for future landscape fire risk assessment and prevention efforts under climate change.

Characteristics and fire-inducing risk analyses of arc faults in low-voltage electrical systems

Arc fault is a prevalent phenomenon in low-voltage residential power systems and the main cause of electrical fires. This paper explores the fire risks associated with arc faults and employs standards from arc fault detection devices (AFDD) to create an arc fault experimental system complemented by an integrated multi-sensor system for ignition related data collection. The objective is to synthetically examine the dynamic characteristics and ignition mechanisms of arc faults. This paper investigates the generation mechanisms, waveforms, and energy characteristics of arc faults, including waveform distinctions across different load types and patterns of energy variation during arcing. Data from the multi-sensor system become the cornerstone of the correlation analysis between the current, energy, and ignition phenomena, facilitating the construction of an ignition probability model based on maximum likelihood estimation. The reliability of the model is verified across various datasets with a 95 % confidence interval, offering a quantitative fire-inducing risk assessment of arc faults. Furthermore, the paper assesses the AFDD standards’ break time limits and evaluates the performance of seven AFDD prototypes based on the proposed model, demonstrating the effectiveness of this technology in mitigating the risks of fires induced by arc faults. This study systematically analyzes the behaviors and ignition mechanisms of arc faults, thereby providing scientific evidence and data that support the enhancement of arc fault detection technologies and the development of fire prevention strategies.

Effect of Local Fire Risks and Preventive Measures on the Installation of Residential Firefighting Systems in Detached House

This study analyzes the major factors affecting the installation of residential firefighting systems in detached houses. In particular, it investigates the dependence of local fire risk and local government prevention efforts, along with housing and household characteristics, on the type of installation. This study used data from the 2020 Population and Housing Census, the first nationwide survey on residential firefighter system installation, and employed multinomial logistic regression analysis. The analysis results revealed that less than half of households had installed all firefighting systems, where the installation rate differed significantly by region. Housing and household characteristics also influenced the installation type. Local fire risk as well as the local government’s fire prevention policies positively affect the installation of residential firefighting systems.

청양지역으로 본 백제 가마의 운영과 의미

Evidence has confirmed the existence of kiln sites that produced a variety of objects in the Cheongyang area over different periods. They are so large in numbers that they account for approximately 42% of the entire relics. There were especially many kiln sites built in the Cheongyang area because of its geographical conditions necessary for production and its historical context close to the capital city. All of the Baekje kiln sites were built in “Yeolgi-hyeon” and supplied necessary objects to the capital cities of Woongjin~Sabi. The kiln sites of Hagam-ri made and provided ceremonial pottery needed in the capital cities. Those of Gwanhyeon-ri and Wangjin-ri made and supplied roof-tiles representing each group. Those of Bonui-ri were characteristic in that they produced pedestals for Buddha statues. The Baekje kiln sites in the Cheongyang area were managed in three major ways including the production of roof-tiles along with pottery, the changing supply-demand relations, and the intentional transformation of roof-end tiles patterns. They were the results of flexible responses to roof-tiles demand in the composition and moving of the production group and the efficiency of kiln operation. Baekje built kiln sites in the Cheongyang area because the capital city lacked the conditions to build a production relic. The mountains around the capital city had tombs and fortresses built, and it was important to minimize the fire risk of the major buildings within the capital city walls. The opposite side of the Geumgang-River was thus highlighted as an important production site, and the Cheongyang area followed this historical context.

A Robotic Teleoperation System with Integrated Augmented Reality and Digital Twin Technologies for Disassembling End-of-Life Batteries

Disassembly is a key step in remanufacturing, especially for end-of-life (EoL) products such as electric vehicle (EV) batteries, which are challenging to dismantle due to uncertainties in their condition and potential risks of fire, fumes, explosions, and electrical shock. To address these challenges, this paper presents a robotic teleoperation system that leverages augmented reality (AR) and digital twin (DT) technologies to enable a human operator to work away from the danger zone. By integrating AR and DTs, the system not only provides a real-time visual representation of the robot’s status but also enables remote control via gesture recognition. A bidirectional communication framework established within the system synchronises the virtual robot with its physical counterpart in an AR environment, which enhances the operator’s understanding of both the robot and task statuses. In the event of anomalies, the operator can interact with the virtual robot through intuitive gestures based on information displayed on the AR interface, thereby improving decision-making efficiency and operational safety. The application of this system is demonstrated through a case study involving the disassembly of a busbar from an EoL EV battery. Furthermore, the performance of the system in terms of task completion time and operator workload was evaluated and compared with that of AR-based control methods without informational cues and ‘smartpad’ controls. The findings indicate that the proposed system reduces operation time and enhances user experience, delivering its broad application potential in complex industrial settings.

A Safe Fiber-Optic-Sensor-Assisted Industrial Microwave-Heating System.

Industrial microwave-heating systems are pivotal in various sectors, including food processing and materials manufacturing, where precise temperature control and safety are critical. Conventional systems often struggle with uneven heat distribution and high fire risks due to the intrinsic properties of microwave heating. In this work, a fiber-optic-sensor-assisted monitoring system is presented to tackle the pressing challenges associated with uneven heating and fire hazards in industrial microwave systems. The core innovation lies in the development of a sophisticated fiber-optic 2D temperature distribution sensor and a dedicated fire detector, both designed to significantly mitigate risks and optimize the heating process. Experimental results set the stage for future innovations that could transform the landscape of industrial heating technologies toward better process quality.

Experimental Investigation of the Combustion Characteristics and Thermal Hazards of Methylsilyl-Modified Silica Aerogels

The thermal safety of hydrophobic silica aerogels (SAs) is essential to thermal insulation applications. Herein, trimethylchlorosilane (TMCS), dimethyldichlorosilane (DMDCS), and methyltrichlorosilane (MTCS) were employed as surface modifiers to prepare three different methylsilyl-modified SAs (i.e., TSA, DSA, and MSA) and their combustion characteristics and thermal hazards were experimentally studied in detail. The cone calorimeter test found that the three SAs have similar combustion processes and the variations in ignition time and fire spread rate with the heat flux obey simple logarithmic and linear relationships, respectively. It further found that TSA has the most methylsilyl groups on silica skeletons and thus has the largest heat release, followed by DSA and MSA in turn, implying that TSA has the greatest fire hazard among the three SAs. These results further demonstrate that the type and quantity of methylsilyl groups on the skeletons of SAs significantly affect the thermal hazard of methylsilyl-modified SAs. In addition, the combustion mechanism of the methylsilyl-modified SAs is discussed. In total, this work experimentally studies the combustion characteristics of methylsilyl-modified SAs and compares their thermal hazards, clarifying the potential fire risk of methylsilyl-modified SAs in practical thermal insulation applications.

The Use of Geographic Information Systems and Multi-Criteria Decision-Making Methods in the Creation of Forest Fire Susceptibility Maps: A Literature Review

Forest fires are natural disasters that cause significant environmental, economic, and social damage worldwide. This study provides a literature review examining how Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods are utilized in the prevention of forest fires and the identification of high-risk areas. GIS, as a system used for the collection and analysis of spatial data, enables the consideration of various factors influencing fire risk, such as climate change, topography, vegetation, and weather conditions. The spatial analysis capabilities offered by GIS play a critical role in identifying regions with high fire susceptibility when generating fire risk maps. Additionally, MCDM methods contribute significantly to the decision-making process by allowing the evaluation of multiple criteria in fire risk analysis. Logistic Regression and Frequency Ratio, which are frequently employed in the literature, are widely used in fire risk analysis and improve the accuracy of susceptibility maps. Furthermore, MCDM methods have been proven effective in estimating the likelihood of forest fire occurrences and identifying fire-prone areas. The integration of GIS and MCDM methods allows for more precise identification of risk zones and supports the development of fire prevention strategies. This literature review highlights the advantages of utilizing GIS and MCDM in the production of forest fire susceptibility maps and suggests that these methods may have broader applications in future research. The effective use of technology in combating forest fires enhances the accuracy of fire risk assessments, contributing significantly to environmental protection efforts.

Landscape-level analysis of disturbance regimes in Chinnar Wildlife Sanctuary, a protected area of Western Ghats, India

ABSTRACT The degradation, fragmentation, and depletion of forests have occurred all over the world, simultaneously, with the exponential growth of the human population. The present study assesses the forest disturbance regime of Chinnar Wildlife Sanctuary (CWS), an ecologically significant biodiversity hotspot of Western Ghats (WG) through satellite image-based forest and land cover mapping and landscape metric analysis. The on-screen analysis based on field knowledge, facilitated, mapping of seven types of vegetation cover. The majority of forest area was covered by dry deciduous forest (39.2 km2 or 43.15%), whereas the minimum area was covered by disturbed forest (2.05 km2 or 2.26%). The disturbance index (DI) was calculated by linearly combining the land use land cover (LULC), road and settlement closeness, fire risk, porosity, patchiness interspersion, normalized difference vegetation index (NDVI), and fragmentation through a probabilistic weight-based linear additive model. The values of the DI (120–310) reclassified into four classes viz. low, moderate, high, and very high. The results revealed that 74.62 km2 (82.52%) of the forest area has low to moderate disturbance, while the rest of the forest area that is 15.8 km2 (17.48%) has high to very high disturbance. These analyses are based on remote sensing and GIS crucial for forest conservation and management strategies formulation.

Research on fire accident prediction and risk assessment algorithm based on data mining and machine learning

Forest fire is a kind of natural disaster that is destructive, easy to spread, and difficult to extinguish. It greatly harms the balance of ecosystem and human life and property. The prediction and risk assessment of forest fire accidents can find forest fires as early as possible and then take corresponding remedial measures; the loss of forest fires will be minimized. This paper first collected the relevant data information in the Lesser Hinggan Mountain region, established the fire driving factor database, and then analyzed the impact and distribution characteristics of forest fire driving factors such as temperature and rainfall. Finally, a fire accident prediction model was built based on a deep neural network, and the model’s performance was compared with the SVM and RF models. The analysis results show that this paper’s fire accident prediction model is more accurate.

Future climate-driven escalation of Southeastern Siberia wildfires revealed by deep learning

The Southeastern Siberia (SES) region has recently experienced increasingly extensive wildfires in spring, which have threatened its large carbon sequestration capacity from vast forests and peatlands. However, the underlying mechanisms propelling the increased fires and their potential responses to future climate change remain unclear. Here, by using reanalysis data and climate model output together with a deep learning model, we explore the relationship between positive-phase North Atlantic Tripole (NAT) sea-surface temperature anomalies and SES wildfire increases and project the future trend in SES wildfire intensities under climate change. We found that the positive-phase April NAT enhances the Siberian anticyclone, causing increased temperatures and snowmelt via strengthened transport of warm-air advection into the SES region. The latter process heightens the exposure of local high-density peatlands to favorable conditions for fire ignition and leads to more intensive wildfire incidents. We further demonstrate that the projected NAT variations can drive interdecadal changes in future April SES wildfires. With future phase shifting of NAT modes under global warming, the regionally averaged burned area in SES could be increased by 47–62% under different warming scenarios from 1982–2014 to 2015–2100. Our findings reveal the climate-driven escalation of future wildfires in SES in the context of global warming and call for active and urgent fire management strategies to mitigate the fire risk.

A dynamic fire risk assessment method for compact historic villages based on the improved FRAME

There are a large number of high-value heritage sites in the historic villages preserved in the mountainous areas of southern China. These sites are exposed to major fire risks due to their compact layout and wooden building materials. By improving the fire risk assessment method for engineering (FRAME), we proposed a dynamic fire risk assessment method for compact historic villages. On one hand, a balance between universality and pertinence was established regarding the assessment indexes and weighting system by integrating general factors with unique regional factors. On the other hand, a connecting link between prescriptive fire codes and performance-based design adjustments was established through the cyclic operation path of assessment-intervention-reassessment. Xiaozhai village, which has experienced serious fire incidents, was selected as an example. Its original fire risk, established fire interventions, actual fire results, and optimized fire protection plan were compared and analyzed to validate the effectiveness and reliability of the assessment method. This study reveals the correlation between various factors of the village and building fire risk. Among these factors, building proximity, traditional customs, and fire protection layout, which are not included in the original FRAME, have a significant impact on fire risk. The results of this study can serve as a reference for fire prevention in historic villages with similar characteristics worldwide. Additionally, the proposed methodology can provide insights into the development of fire risk assessment methods for various types of historical settlements, thus promoting the sustainable development of built heritage in these settlements.

A fire risk pre-warning framework for high-rise buildings based on unascertained method

The growing global interest in preventing and controlling fires in high-rise buildings reflects the increasing significance of this issue today. This research aims to establish an early warning framework for fire risk in high-rise buildings. Firstly, considering the importance of a scientific indicator system for the application of the model, this study combines the event analysis method with the building design fire code to identify 11 key risk factors that have a far-reaching impact on the prevention of fires in high-rise buildings. Based on identifying the risk factors, a high-rise building fire risk warning tree is also established, which scientifically solves the problem of the indicator system of the warning object. Subsequently, in response to the various complex issues arising from the uncertainty of fire occurrence in high-rise buildings, this study adopts the unascertained method to model the fire risk of high-rise buildings for early warning. In addition, the developed methodology was empirically validated through case studies and analyses of empirical data on fire risks in nine representative high-rise buildings. The results of the unascertained method were also compared with the results of the K-means method, from which it was concluded that the unascertained method can predict building fires more accurately. The research results provide a reliable decision support system for fire disaster prevention and control in high-rise buildings.

Investigating the sustainable energy generation potential of an invasive weed: Lantana camara.

Lantana camara, one of the world's top ten most invasive species, was initially cultivated for ornamental use. However, it spread uncontrollably across the fallow areas and agricultural lands, threatening approximately 44% of Indian forests. Its invasion disrupts ecosystems by suppressing nearby plant growth through allelopathy and poses toxicity risks to grazing ruminants. It significantly increases forest fire risk by adding large amounts of combustible biomass, particularly dried L. camara. Despite efforts to control it using mechanical, chemical, and biocontrol methods, the results have been largely unsatisfactory, with associated costs estimated at $18,000 per square kilometre. Considering these challenges, recent research explored the potential of L. camara as a bioenergy resource. TheL. camarabriquettes exhibit a heating value of approximately 20MJ/kg with a low sulphur (0.5%), nitrogen (1%), and ash content (2%), making them suitable for decentralised energy production. Furthermore, bioethanol production fromL. camarahydrolysate has shown promising results, yielding 0.33g/g withPichia stipitisand0.47g/g with Saccharomyces cerevisiae, which is comparable to other lignocellulosic feedstocks. Additionally, the gasification of L. camara using a downdraft gasifier produced syngas with a lower heating value (LHV) of 6.4MJ/Nm3. These findings demonstrate that using L. camara for bioenergy production presents a dual solution, addressing the growing demand for renewable energy and managing invasive species. This review aims to critically evaluate the potential and challenges associated with the different energy production pathways for L. camara, highlighting its role in sustainable energy generation.

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

Stabilizing zinc anodes by a solvation sheath modification with toluenesulfonate additive

As an advanced electrochemical energy storage device, aqueous zinc-ion batteries offer high theoretical capacity and energy density, employing water-based electrolytes to reduce fire and explosion risks. However, zinc tends to form dendrites during charge-discharge processes, leading to reduced electrode surface area and shortened cycling lifespan. In this study, we propose the use of a commonly used and cost-effective additive, sodium p-toluenesulfonate (OTM), to stabilize zinc anodes. Experimental and theoretical simulations demonstrate that OTM can enter the solvation sheath of Zn2+, reduce the activity of nearby H2O molecules, facilitate uniform Zn2+ deposition, and suppress zinc dendrite formation. Therefore, Zn//Zn symmetric batteries utilizing ZnSO4 (ZSO) electrolytes containing OTM achieved outstanding long-term performance exceeding 1700 h at 1 mA cm−2, significantly surpassing those employing ZSO electrolytes. Additionally, Zn//VO2 full batteries with OTM additives exhibited enhanced cycling stability and an initial discharge capacity of 180 mAh g−1 at 3 A g−1, demonstrating its superior application potential.

Transforming fire protection: AI's evolving role in proactive and reactive applications

Abstract. This paper comprehensively reviews the applications of artificial intelligence (AI) in the field of fire protection. It traces the historical development of AI, from its origins in the 1930s and 1940s to its current state, focusing on major milestones and algorithms that have shaped the field. The paper discusses the evolution of AI, particularly in terms of machine learning, deep learning, and the emergence of various models such as deep neural networks (DNNs), transformer models, and graph neural networks (GNNs). The application of AI in fire protection is explored in two primary categories: proactive and reactive applications. Proactive applications encompass AI's role in decision-making, construction, and operation, including the use of machine learning for environmental analysis, building information modelling (BIM) for design optimization, 3D printing and intelligent robots in construction, and intelligent fire facility management. Reactive applications focus on early fire detection and monitoring, especially in forests, utilizing terrestrial, aerial, and satellite systems equipped with advanced sensors and deep learning techniques. This paper highlights the significant contributions of AI in mitigating fire risks, improving fire detection and response, and enhancing the safety and efficiency of fire protection systems.

Underground rescue path planning based on a comprehensive risk assessment approach

Fire incidents in underground environments, such as subway stations and shopping malls, pose significant hazards due to restricted ventilation and confined spaces. These conditions complicate rescue operations, particularly given the unpredictable nature of fires. Effective integration of fire risk assessment into rescue path planning is essential for ensuring both safety and operational efficiency. However, fire risk is inherently complex, varying across both temporal and spatial dimensions, and accurate assessment depends on precise fire situation inference. Despite advancements in fire simulation technologies, inconsistencies in geometric structures between computational units limit seamless integration with path planning models. Consequently, many existing studies rely on simplistic and less reliable linear fire inference models, compromising the safety of rescue operations. This paper addresses these challenges by proposing an underground rescue path planning method based on a comprehensive fire risk assessment, aimed at enhancing both safety and operational efficiency. A fire risk assessment approach, driven by fire situation inference, is introduced, employing a novel grid-matching transformation to capture the spatio-temporal dynamics of fire conditions using high-precision simulation software. Additionally, an improved A* algorithm is developed for real-time rescue path optimization, minimizing path risk based on the results of the risk assessment. The proposed method is validated through a detailed case study, demonstrating its effectiveness and reliability.

Research on combustion characteristics and fire risk of ZR-BVR wires with different thermal aging degrees

Research on combustion characteristics and fire risk of ZR-BVR wires with different thermal aging degrees