Fire Occurrence Research Articles

Study on Mechanical Properties of Tunnel Flame Retardant Asphalt Pavement Considering the Ablative Effect

With the widespread utilization of composite pavements in highway tunnel projects, the flammability issue of asphalt layers has garnered increasing attention. Previous research has demonstrated that the addition of flame retardants effectively reduces the occurrence of pavement fires. However, there are few studies combining asphalt flame retardant technology with tunnel composite pavement structures. Therefore, this paper, considering the context of actual engineering projects, uses ABAQUS software to analyze and calculate the mechanical response of the tunnel composite pavement, considering the thickness of various asphalt layers under the ablation depth. Key design indexes such as shear stress and lower tensile strain of the asphalt layer, the bottom tensile stress of the cement concrete subgrade, and interlayer shear stress between the asphalt layer and cement concrete subgrade of composite pavements are analyzed using the nonlinear finite element analysis method. The findings enable the determination of the optimal thickness of the composite pavement asphalt layer combination after ablation, thus contributing to enhancing the safety of tunnel pavements.

SIMULATION THE PROCESSES OF ORGANIC MATERIALS DESTRUCTION AND COMBUSTION PROCESSES CONDITION OF FIRE INTENSEICATION

Problem statement. The rapid occurrence and development of fires are caused by the occurrence of combustible materials and structures in their focus. This complicates, and in some cases makes impossible, effective firefighting, evacuation, and rescue of people and property. In order to correctly assess fire resistance, effectively increase fire resistance, and change the flammability of materials, it is necessary to study the materials burning process. The purpose of the article. To study of the combustion of easily dispersible substances and the development of gaseous reactions and their combination using the example of wood combustion. Methods. We use mathematical modeling of combustion and destructive processes under the high temperatures, taking into account the rules of thermodynamics. Research results. The conducted analysis showed that the selection of the main stages in the simulation of wood combustion is much less developed than in the case of the combustion of gas systems. It is especially difficult to do this to describe the combustion of wood, on the surface of which there is an interaction of masses (and heat) during the combustion of individual components, and the size of the front surface per unit cross-section of the sample may also change over time. Therefore, the definition of the main stage in wood burning should be used mainly in the qualitative analysis of the process, and quantitative models of wood burning should be built, if possible, taking into account all the determining factors characterizing the phenomena. Scientific novelty. For the first time, on the basis of the modeling of organic material combustion is proposed. We determine the patterns of destructive processes, which, depending on the temperature, have common patterns of qualitative and quantitative characteristics. The chemical phasing and its connection with the physical phasing of the transformation of combustible organic materials dispersed in the surface layer with the overall thermal effect of the combustion wave were determined. Practical value. The identification of stages during the wave burning of wood allows for a qualitative display of processes depending on the spatial zone and its distance from the gas zone, which allows predicting the development of protective means of organic materials against combustion.

The importance of geography in forecasting future fire patterns under climate change

An increasing amount of California's landscape has burned in wildfires in recent decades, in conjunction with increasing temperatures and vapor pressure deficit due to climate change. As the wildland-urban interface expands, more people are exposed to and harmed by these extensive wildfires, which are also eroding the resilience of terrestrial ecosystems. With future wildfire activity expected to increase, there is an urgent demand for solutions that sustain healthy ecosystems and wildfire-resilient human communities. Those who manage disaster response, landscapes, and biodiversity rely on mapped projections of how fire activity may respond to climate change and other human factors. California wildfire is complex, however, and climate-fire relationships vary across the state. Given known geographical variability in drivers of fire activity, we asked whether the geographical extent of fire models used to create these projections may alter the interpretation of predictions. We compared models of fire occurrence spanning the entire state of California to models developed for individual ecoregions and then projected end-of-century future fire patterns under climate change scenarios. We trained a Maximum Entropy model with fire records and hydroclimatological variables from recent decades (1981 to 2010) as well as topographic and human infrastructure predictors. Results showed substantial variation in predictors of fire probability and mapped future projections of fire depending upon geographical extents of model boundaries. Only the ecoregion models, accounting for the unique patterns of vegetation, climate, and human infrastructure, projected an increase in fire in most forested regions of the state, congruent with predictions from other studies.

Past Fire and Vegetation Change in the Hyperdiverse Forests of the Ecuadorian Amazon.

The Ecuadorian Amazon holds more biodiversity than most other places on Earth. Palms are a particularly dominant component of the vegetation; however, it remains unknown to what degree the pattern has persisted through time. Here, we investigate the persistence of palm dominance through time and the degree to which past human activities (e.g., fire, cultivation, and forest opening) have affected changes in palm abundances across five regions of the Ecuadorian Amazon. We analyzed soil cores (40-80 cm depth) from each region for charcoal (evidence of past fire) and phytoliths (evidence of past vegetation change). The timings of fires (based on 14C radiocarbon dates), the occurrence, recurrence, and number of fires (based on charcoal presence and abundance in samples), and the amount of change in palm abundances (based on phytoliths) varied within and between the studied regions. The charcoal and phytolith results indicate the presence of low levels of past human activity at all sites. Our results show that patterns of modern palm hyperdominance found in Amazonian forests have not been persistent through time, and that even low levels of past human activities can affect palm abundance.

Urban Fire Risk Dynamics and Mitigation Strategies in Shanghai: Integrating Spatial Analysis and Game Theory

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.

Multi-Year Continuous Observations of Ambient PM2.5 at Six Sites in Akure, Southwestern Nigeria

The spatial–temporal variations of fine particulate matter (PM2.5) in Akure, a city in southwestern Nigeria, are examined based on multi-year continuous observations using low-cost PM2.5 sensors at six different sites. The average annual concentration of PM2.5 across these sites was measured at 41.0 µg/m3, which surpassed both the Nigerian national air quality standard and the World Health Organization air quality guideline level. PM2.5 levels were significantly higher during the dry season (November–March), often exceeding hazardous levels (over 350 µg/m3), than during the wet season. The analyses of trends in air mass trajectories and satellite data on fire occurrences imply that the transport of dust and accumulation of PM2.5 originating from local/regional open burning activities played crucial roles in increased PM2.5 concentrations during the dry season. Further, site-to-site variations in the PM2.5 levels were observed, with relatively high concentrations at less urbanized sites, likely due to high local emissions from solid fuel combustion, waste burning, and unpaved road dust. Diurnal patterns showed morning and evening peaks at less urbanized sites, accounting for an estimated 51–77% of local emissions. These results highlight the importance of local emission sources in driving spatial–temporal PM2.5 variations within the city and the need for targeted mitigation strategies to address the significant air pollution challenges in Akure and similar regional cities in West Africa.

Predicting the Integrated Fire Resistance of Wildland–Urban Interface Plant Communities by Spatial Structure Analysis Learning for Shanghai, China

Abstract: Fire is a prevalent hazard that poses a significant risk to public safety and societal progress. The continuous expansion of densely populated urban areas, exacerbated by global warming and the increasing intensification of urban heat islands, has led to a notable increase in the frequency and severity of fires worldwide. Incorporating measures to withstand different types of calamities has always been a crucial aspect of urban infrastructure. Well-designed plant communities play a pivotal role as a component of green space systems in addressing climate-related challenges, effectively mitigating the occurrence and spread of fires. This study conducted field research on 21 sites in the green belt around Shanghai, China, quantifying tree morphological indexes and coordinate positions. The spatial structure attributes of different plant communities were analyzed by principal component analysis, CRITIC weighting approach, and stepwise regression analysis to build a comprehensive fire resistance prediction model. Through this research, the relationship between community spatial structures and fire resistance was explored. A systematic construction of a prediction model based on community spatial structures for fire resistance was undertaken, and the fire resistance performance could be quickly judged by easily measured tree morphological indexes, providing valuable insights for the dynamic prediction of fire resistance. According to the evaluation and ranking conducted by the prediction model, the Celtis sinensis, Sapindus saponaria, Osmanthus fragrans, Koelreuteria paniculata, and Distylium racemosum + Populus euramericana ‘I-214’ communities exhibited a high level of fire resistance. On the other hand, the Koelreuteria bipinnata + Ligustrum lucidum, Ginkgo biloba + Camphora officinarum + Ligustrum lucidum, and Ligustrum lucidum + Sapindus saponaria communities obtained lower scores and were positioned lower in the ranking. It is emphasized that the integration of monitoring and regulation is essential to ensure the ecological integrity and well-being of green areas in the Wildland–Urban Interface.

Sensitivity of Fire Indicators on Forest Inventory Plots Is Affected by Fire Severity and Time since Burning

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators.

Применение методов пространственного анализа при оценке степени горимости лесов Хабаровского края

Forest fires are the dominant factor in the transformation of natural landscapes in the boreal zone. The area of the Khabarovsk Territory, 93.6 % of which belongs to the forest fund lands, currently has a forest cover of only 66.5 %, which is largely due to forest fires. This makes urgent the problem of reducing the forest fire frequency in the region, including in terms of assessing the actual fire frequency for predicting the risks of landscape fires. The article shows the possibilities of using spatial analysis methods of the Spatial Analyst module of the ArcGIS 10.8 software package to assess the degree of actual forest fire frequency based on calculating the number of fires per 1 million and 250 thousand ha and the area of fires per 1000 ha for a sliding window of a given size. Based on the data from the Remote Monitoring Information System of the Federal Forestry Agency for 2000–2020 and the data on the number of fires from the Fire Information for Resource Management System website for 2000–2021, an assessment of the actual fire frequency for the Khabarovsk Territory has been carried out. The analysis of the results reflects the high spatial and seasonal heterogeneity of the spread of landscape fires in the territory and their proximity to transport and residential structures. Areas with high fire frequency often do not coincide with the boundaries of forestry enterprises, and the maximum indicators are typical for non-forest areas that are not covered by forestry statistics. For different seasons of the year, along with quantitative differences, spatial shifts of firing points and areas with their high density are also observed. Most of the lands in the region (88.3 %) in terms of fire danger belong to the “low” and “below medium” fire frequency categories, which is primarily due to the low degree of development of these areas. The assessment of the degree of fire frequency in the territory on the basis of annual and seasonal maps of the density of firing points, calculated using the sliding window method, makes it possible to fairly objectively and in detail reflect the average long-term risk of fire occurence outside of reference to forestry boundaries. In combination with a map of the distribution of forests by natural fire hazard classes based on pyrological characteristics, the results of the reseаrch can become the basis for calculating the background fire risk, reflecting the risk of fire occurrence and development.

Probabilistic Path Planning for UAVs in Forest Fire Monitoring: Enhancing Patrol Efficiency through Risk Assessment

Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

Active Wildland Fires in Central Chile and Local Winds (Puelche)

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper’s results will be used to prevent future active fire occurrences in the CC.

Forest Fire Prediction Based on Time Series Networks and Remote Sensing Images

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.

Investigation into the overcurrent failure and combustion characteristics of copper‐clad aluminum conductors

AbstractElectrical fires perennially rank first in fire occurrence types, with conductor overcurrent being one of the main inducements. This topic draws significant attention from scientific researchers and fire investigators. To understand the overcurrent fault and combustion characteristics of copper‐clad aluminum conductors, this paper examines 2.5 mm2 copper‐clad aluminum conductors that meet national standards, investigating morphological changes, temperature variations in the core and insulation layer, and flame propagation patterns under overcurrent conditions. Experiments using an electrical fault simulation device were conducted to study overcurrent failures of copper‐clad aluminum conductors under 52.5–105 A conditions. The results indicate that when the current exceeds 67.5 A, the conductor undergoes a series of changes during energization, including smoking, expanding, carbonizing, burning, and breaking; at 52.5 A, the insulation layer reaches thermal equilibrium at 150 s without combustion; for currents between 60–67.5 A, wire core temperature variations can be divided into three stages; at 75 A, the insulation layer reaches thermal equilibrium 10s before breaking; currents above 82.5 A see a sharp increase in temperature in both the core and insulation layer before the conductor breaks; above 97.5 A, the conductor first breaks and then burns. The research results have significant theoretical value in improving the scientific rigor of fire accident investigations and forensic evidence examinations.

A Lab-Scale Experiment on the Spontaneous Combustion Behavior of Chronic Naturally–Oxidized Coal in Re-Mining Coal Mine

ABSTRACT Spontaneous combustion of coal in re-mining coal mine is a serious threat to the safety recovery of coal resources. In this paper, six coal samples, including two raw bituminous coal samples from the new mining working face and four oxidized bituminous coal samples from the re-mining working face, are selected. The pore structures, thermogravimetric properties, and spontaneous combustion characteristics of raw coal and oxidized coal are studied. The pore structures show that the surface of oxidized coals has more cracks and higher macropore volume and porosity as compared with raw coal. The thermogravimetric experiments demonstrate that the oxidized coal has a higher combustion rate and turns to the combustion reaction zone in advance. Moreover, the oxidized coals perform a better combustion property by showing a comprehensive combustion index of 6.7 × 10−9 min−2·°C−3 and 8.9 × 10−9 min−2·°C−3, higher than the raw coal of 4.0 × 10−9 min−2·°C−3. The spontaneous combustion characteristics show that the oxidized coal performs a greater rate of oxygen consumption and more oxidation products released than raw coal. The CPT of raw coal is higher than 157°C, while the CPT of oxidized coal is between 132°C and 140°C. The research results can provide a risk assessment method for coal spontaneous combustion in the re-mining coal mine, and provide guidance for avoiding the occurrence of coal fires.

On the prediction of rare events when sampling from large data

When modeling rare events using logistic regression, independent samples of event occurrence (ones) and nonoccurrence (zeros) are commonly taken from large datasets in order to fit models efficiently. A deterministic offset may then be included in the model to compensate for that sampling method. We propose a more complex sampling approach using stratified sampling within the sets of ones and zeros to ensure that we may sample more zeros from strata of interest. This design may avoid situations in which a random sample of zeros fails to capture the range of a key covariate. We employ sampling weights along with stratum-specific intercepts to obtain unbiased estimates of the logistic regression coefficients (including the intercept) and their standard errors. We use simulation to show that this method provides unbiased parameter estimates comparable with those of maximum likelihood. We also illustrate an application of this method to wildland fire occurrence prediction in a study area in northwestern Ontario, Canada.

Research Trends in Wildland Fire Prediction Amidst Climate Change: A Comprehensive Bibliometric Analysis

Wildfire prediction plays a vital role in the management and conservation of forest ecosystems. By providing detailed risk assessments, it contributes to the reduction of fire frequency and severity, safeguards forest resources, supports ecological stability, and ensures human safety. This study systematically reviews wildfire prediction literature from 2003 to 2023, emphasizing research trends and collaborative trends. Our findings reveal a significant increase in research activity between 2019 and 2023, primarily driven by the United States Forest Service and the Chinese Academy of Sciences. The majority of this research was published in prominent journals such as the International Journal of Wildland Fire, Forest Ecology and Management, Remote Sensing, and Forests. These publications predominantly originate from Europe, the United States, and China. Since 2020, there has been substantial growth in the application of machine learning techniques in predicting forest fires, particularly in estimating fire occurrence probabilities, simulating fire spread, and projecting post-fire environmental impacts. Advanced algorithms, including deep learning and ensemble learning, have shown superior accuracy, suggesting promising directions for future research. Additionally, the integration of machine learning with cellular automata has markedly improved the simulation of fire behavior, enhancing both efficiency and precision. The profound impact of climate change on wildfire prediction also necessitates the inclusion of extensive climate data in predictive models. Beyond conventional studies focusing on fire behavior and occurrence probabilities, forecasting the environmental and ecological consequences of fires has become integral to forest fire management and vital for formulating more effective wildfire strategies. The study concludes that significant regional disparities in knowledge exist, underscoring the need for improved research capabilities in underrepresented areas. Moreover, there is an urgent requirement to enhance the application of artificial intelligence algorithms, such as machine learning, deep learning, and ensemble learning, and to intensify efforts in identifying and leveraging various wildfire drivers to refine prediction accuracy. The insights generated from this field will profoundly augment our understanding of wildfire prediction, assisting policymakers and practitioners in managing forest resources more sustainably and averting future wildfire calamities.

Prediction Model of Spontaneous Combustion of Lignite in Zhalainuoer Mining Area.

A programmed temperature-increase experiment was conducted on coal samples from four coal mines in the Zhalainuoer mining area to improve the accuracy of predicting and forecasting lignite spontaneous combustion. The gases produced during the coal tests were analyzed, and logistic, exponential, Boltzmann, and fourth-degree polynomial functions were selected to develop predictive models for the gas data. Additionally, the Boltzmann function was used to predict the occurrence of fire. The results revealed that the initial appearance temperature of CO was approximately 50 °C, and it exhibited an exponential growth trend with increasing temperature. The initial appearance temperature of C2H4 was approximately 140 °C, which could serve as an indicator of coal entering the accelerated oxidation stage. Applying the selection principle to gas indicators, CO and C2H4 were identified as single gas indicators for lignite spontaneous combustion in the Zhalainuoer mining area, whereas CO/CO2 and C2H4/C2H6 were identified as composite gas indicators. Among the four functions, the Boltzmann function model exhibited the best fitting effect for CO and CO/CO2 within the temperature range of 0-200 °C. The values of the four parameters (A 1, A 2, dx, and x 0) were determined based on their statistical characteristics, and a functional equation describing the relationship between gas concentration and coal temperature was derived. This indicates that the Boltzmann function model can be effectively used to predict the spontaneous combustion of lignite in the Zhalainuoer mining area.

Vegetation Warming and Greenness Decline across Amazonia during the Extreme Drought of 2023

In 2023, most parts of the world experienced exceptional heat. In particular, anomalous warm temperatures and heatwave events were evidenced across South America during the second half of the year. The situation was particularly critical in the Amazon region in terms of not only hydrometeorological drought but also ecological and socioeconomic impacts. In this study, remote-sensing data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to observe the changes in temperature and vegetation across Amazonia during the exceptional drought of 2023. This analysis was based on anomalies in the land surface temperature (LST) and vegetation indices: the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). The amplitude of the LST (AMP-LST), an indicator of the energy partitioning between the latent and sensible heat flux, and fire counts were also considered. The results show widespread and extreme warming across Amazonia during the austral spring in 2023, accompanied by a decline in vegetation greenness, water stress conditions across northern Amazonia, and an enhanced fire occurrence across central and northern Amazonia.

Fire protection priorities in the oak forests of Iran with an emphasis on vertebrate habitat preservation

This study examines the impact of fire incidents on wildlife and habitats in the western oak forests of Iran (Zagros region). These forests are globally recognized for their exceptional biodiversity but are frequently threatened by wildfires. To achieve this, the study uses the space–time scan statistics permutation (STSSP) model to identify areas with a higher frequency of fires. The study also analyzes the effects of fires on the Zagros forests from 2000 to 2021 using remote-sensing MODIS data. Also, to understand the elements at risk of fire, burned areas were assessed based on the richness of vertebrate species, determined by the distribution of 88 vertebrate species. The results show that the annual fire rate in the Zagros forests is 76.2 (fire occurrences per year), calculated using the Poisson distribution. Findings show the highest fire rates are found in the northwest and a part of the south of the Zagros. The northwest of the Zagros also has the largest number of single fires and clusters, indicating a wide spatial distribution of fire in these regions. On the other side, it was unexpectedly found that these regions have the richest number of species and higher habitat value. The results demonstrate a significant correlation between the value of the habitat and the extent of burned areas (p < 0.05). The study also reveals that the greatest impact of fires is on small vertebrates. The overlap of frequent fire spots with the richest regions of Zagros oak forests in terms of vertebrate diversity emphasizes the need for strategic forest risk reduction planning, especially in these priority zones.

On fire risk reduction when operating individual households

The article aims to elaborate a technical means for reducing fire occurrence in internal electrical networks of individual households. General scientific methods of numerical analysis, electric circuit theory, and prediction theory were used. It was determined that the operating mode of the electric network under study is characterized by a significant unbalanced power consumption due to the asymmetry and non-sinusoidality of phase currents caused by the occurrence of additional symmetrical components of forward, reverse, and zero sequences. Additional heat losses increase significantly and lead to overheating of the neutral conductor, thus intensifying fire risks. On the basis of measurements and the authors’ software, a simulation modeling of integration of the developed balancing device into the investigated network was carried out. The conducted analysis found that the developed device can reduce additional heat losses and fire risks by over 90%. Using statistical methods of forecasting, regression analysis in particular, a preventive long-term assessment of the level of additional power losses was conducted. Autoregressive equations for predicting the parameter of additional power losses at a certain probability level were derived. MATLAB graphic editor technologies and Excel software were used to visualize changes in the studied indicators. The results can be used by construction companies when designing the internal electrical network of individual households, as well as for researchers developing approaches to reducing additional losses of electrical energy.