Probability Of Fire Occurrence Research Articles

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.

Regional High-Rise Building Fire Risk Assessment Based on the Spatial Markov Chain Model and an Indicator System

High-rise buildings (HRBs) are prone to high fire hazards due to their high occupant density, limited evacuation routes, and high fire load. The indicator system method, as a systematic evaluation method, is widely applied to assess HRB fire risk. However, the method is subjective because the determination of the indicator weights mainly relies on expert experience. In order to reduce the subjectivity of the indicator system method in assessing the fire risk of HRBs, this study proposes a new assessment method by combining the spatial Markov chain model and the indicator system method. In this new method, fire occurrence probability is calculated by the spatial Markov chain model using historical HRB fire accident data. An indicator system is built to characterize the fire consequence by the structure entropy weight method. Subsequently, HRBs in Beijing are used as a case to illustrate the practicality of this approach. Firstly, the spatial Markov chain model is trained and validated using the chi-square goodness-of-fit test based on fire accident data from 2018 to 2023 in Beijing. It was found that the best performance was achieved with the monthly period and the four-state. Then, the distribution of regional fire occurrence probability in April was predicted based on fire accident data in March 2023 in Beijing. It showed that areas with higher fire occurrence probability are mainly located in the central region, especially in the I District. Then, the indicator system was used to evaluate the HRB fire consequence in the I District. The assessment results showed that the areas with more severe fire consequences are mainly located in the II and IV Districts, due to the poor performance of the fire system or the absence of fire protection systems. Coupling the fire occurrence probability and its consequences shows that HRBs with higher fire risk are mainly located in area II and should be carefully supervised for fire management. This developed method can provide some insights into the fire safety management of HRBs and the layout of the fire stations.

Design and Implementation of Intelligent Fire Monitoring System Based on Multi-Sensor Data Fusion

Abstract To reduce the losses caused by fire, this paper proposes an intelligent fire monitoring system based on multi-sensor data fusion. Through the design of the temperature, smoke and flame sensors, based on the weighted least squares WLS and improved fuzzy support degree of similar sensors and dissimilar sensors data fusion, respectively, in the ZigBee wireless network based on the design of the fire remote monitoring and control alarm for monitoring, and add the local storage and emergency module. In addition, whether the router nodes work on the ZigBee wireless network during the fire test depends on multi-sensor data fusion. The test shows that the node communication distance is extended and can reach the acceptable requirements in the range of 70m, the highest probability of fire occurrence is 0.92, the lowest probability is 0.14, the overall probability of occurrence is in the range of 0.4 to 0.6, and the error between the predicted value of the probability of occurrence of the fire and the expected value is within ±0.05.

Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India

The escalating global threat of forest fires, driven by global warming, requires the development of effective prediction systems to mitigate damages. This research focuses on Madhya Pradesh (MP) and Chhattisgarh (CG) states in central India, where forest fire risk has become particularly pronounced. The primary objectives of the study are to quantify and map the spatial and temporal dynamics of forest fires over the period 2001 to 2020, and to predict future fire risks using satellite derived datasets and machine learning techniques. Through a long-term analysis, the study revealed an alarming increase in the number of forest fire incidents in MP and CG. From an average of 1200 and 1000 during 2001 to 2005, the incidents increased to 2800 and 2100 during 2016 to 2020, in MP and CG respectively. To predict forest fire risk, Random Forest machine learning algorithm was adopted utilizing various satellite derived climatic, topographical, and ecological parameters such as temperature, precipitation, solar radiation, NDVI, soil moisture, litter availability, evapotranspiration and terrain parameters (at monthly scale for 20 years). While forecasting fire probability for 2018–2020, the model achieves high accuracy rate of 86.46 % in MP and 93.78 % in CG. The results highlight significant forest fire likelihood regions in the central MP and the Southern CG, identifying areas requiring enhanced fire management strategies. This study has revealed that NDVI and rainfall have played a positive role in restricting the forest fire, and their negative anomaly amplified the fire risk. The study would help forest planners and administrators to characterise vulnerable areas and prioritise their conservation provisions.

Temporal and spatial dynamics in emission of water-soluble ions in fine particulate matter during forest fires in Southwest China

AimsThe aim of this study was to analyze changes in emission of water-soluble ions in fine particulate matter over time and in different southwest forest areas in China based on China’s Forestry Statistical Yearbook and MODIS satellite fire point data.MethodsWe took 6 dominant tree species samples in the southwestern forest region of China and simulated combustion using controllable biomass combustion devices. Based on the spatial analysis method of ArcGIS, combining satellite fire point data and official statistical yearbooks, we analyzed the spatial and temporal dynamics of emissions of water-soluble ions in PM2.5 released by forest fires in southwestern forest areas from 2004 to 2021.ResultsThe total amount of forest biomass combusted in southwest forest areas was 64.43 kt. Among the different forest types, the proportion of burnt subtropical evergreen broad-leaved forest was the largest (60.49%) followed by subtropical mixed coniferous and broad-leaved forest (22.78%) and subtropical evergreen coniferous forest (16.72%). During the study period, 61.19 t of water-soluble ions were released in PM2.5 from forest fires, and the emissions of Li+, Na+, NH4+, K+, Mg2+, Ca2+, F−, Cl−, Br−, NO3−, PO43− and SO42− were 0.48 t, 11.54 t, 2.51 t, 19.44 t, 2.12 t, 2.92 t, 1.94 t, 12.70 t, 1.12 t, 1.18 t, 1.17 t and 4.07 t, respectively. Yunnan was the province with the highest emissions of water-soluble ions in PM2.5 in the southwest forest areas, and the concentration K+ was the highest. Emission of water-soluble ions in Yunnan and Sichuan all showed a significant downward trend, while the overall decrease in Tibet, Chongqing and Guizhou was not significant. The peak emission of water-soluble ions in PM2.5 during forest fires appeared in spring and winter, which accounted for 87.66% of the total emission.DiscussionThis study reveals the spatiotemporal changes in water-soluble ion emissions from forest fires, by studying the spatiotemporal dynamics of water-soluble ions in PM2.5, we can better understand the sources, distribution, and change patterns of these ions, as well as their impact on the atmospheric environment, ecosystems, and climate change. This information is crucial for predicting and managing air pollution, as well as developing effective forest management and environmental protection policies to respond to fires; and hence concerted fire prevention efforts should be made in each province, taking into account the season with higher probability of fire occurrence to reduce the potential impact of fire-related pollutions.

Fire risk modeling: an integrated and data-driven approach applied to Sicily

Abstract. Wildfires are key not only to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by wildfires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean Basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time step, providing spatially and temporally explicit results through the k.LAB (Knowledge Laboratory) software. This software provides an environment for input data integration, combining methods and data such as geographic information systems, remote sensing and Bayesian network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (findable, accessible, interoperable and reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily have a high probability of fire occurrence in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aiding in landscape and fire risk management, under both current and climate change conditions.

Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor

Affected by global warming and increased extreme weather, Hunan Province saw a phased and concentrated outbreak of forest fires in 2022, causing significant damage and impact. Predicting the occurrence of forest fires can enhance the ability to make early predictions and strengthen early warning and responses. Currently, fire prevention and extinguishing in China’s forests and grasslands face severe challenges due to the overlapping of natural and social factors. Existing forest fire occurrence prediction models mostly take into account vegetation, topographic, meteorological and human activity factors; however, the occurrence of forest fires is closely related to the forest fuel moisture content. In this study, the traditional driving factors of forest fire such as satellite hotspots, vegetation, meteorology, topography and human activities from 2004 to 2021 were introduced along with forest fuel factors (vegetation canopy water content and evapotranspiration from the top of the vegetation canopy), and a database of factors for predicting forest fire occurrence was constructed. And a forest fire occurrence prediction model was built using machine learning methods such as the Random Forest model (RF), the Gradient Boosting Decision Tree model (GBDT) and the Adaptive Augmentation Model (AdaBoost). The accuracy of the models was verified using Area Under Curve (AUC) and four other metrics. The RF model with an AUC value of 0.981 was more accurate than all other models in predicting the probability of forest fire occurrence, followed by the GBDT (AUC = 0.978) and AdaBoost (AUC = 0.891) models. The RF model, which has the best accuracy, was selected to predict the monthly forest fire probability in Changsha in 2022 and combined with the Inverse Distance Weight Interpolation method to plot the monthly forest fire probability in Changsha. We found that the monthly spatial and temporal distribution of forest fire probability in Changsha varied significantly, with March, April, May, September, October, November and December being the months with higher forest fire probability. The highest probability of forest fires occurred in the central and northern regions. In this study, the core drivers affecting the occurrence of forest fires in Changsha City were found to be vegetation canopy evapotranspiration and vegetation canopy water content. The RF model was identified as a more suitable forest fire occurrence probability prediction model for Changsha City. Meanwhile, this study found that vegetation characteristics and combustible factors have more influence on forest fire occurrence in Changsha City than meteorological factors, and surface temperature has less influence on forest fire occurrence in Changsha City.

Assessment of wildfires forecast performance in Albania: Case study

The harmful impacts of climate change caused by wildfires are substantially harming the people of mainland Europe, as well as damaging species biodiversity and the ecosystem. It can be minimised by improving the effectiveness of fire risk forecasting and mitigation strategies. The aim of this paper was to investigate the accuracy of forest fire forecasts in Albania produced by the FWI (Fire Weather Index) system. During the summer of 2022, observations and data were collected on expected and actual fires in the prefectures of Albania, which were previously divided into four categories according to the level of fire risk: high, moderate, low, and zero. It was determined that, in the summer of 2022, Albania happened a grand total of 620 wildfires. The data were analysed using two indicators: the probability of fire occurrence for a particular prefecture and the number of fires per prefecture. The analysis revealed varying degrees of accuracy in fire predictions across different prefectures, with higher precision observed in high-risk regions but diminishing as the risk level decreased. The most dependable indicator of forecast accuracy, reaching 75%, was observed in high-risk areas during the month of August. Predicting fire localization within moderate-risk zones consistently achieved results above 50% but fell short of the 60% threshold. Overall, the results confirm the effectiveness of using data to predict the probability of fires for prefectures with a high and increased level of the relevant threat. This will make it possible to more effectively deploy and mobilise the resources needed to overcome them and substantially reduce the losses associated with them

Wildland-urban interface typologies prone to high severity fires in Spain

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016–2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Predicting Grassland Fire-Occurrence Probability in Inner Mongolia Autonomous Region, China

Fires greatly threaten the grassland ecosystem, human life, and economic development. However, since limited research focuses on grassland fire prediction, it is necessary to find a better method to predict the probability of grassland-fire occurrence. Multiple environmental variables impact fire occurrence. After selecting natural variables based on remote sensing data and anthropogenic variables, we built regression models of grassland fire probability, taking into account historical fire points and variables in Inner Mongolia. We arrived at three methods to identify grassland fire drivers and predict fire probability: global logistic regression, geographically weighted logistic regression, and random forest. According to the results, the random forest model had the best predictive effect. Nine variables selected by a geographically weighted logistic regression model exercised a spatially unbalanced influence on grassland fires. The three models all showed that meteorological factors and a normalized difference vegetation index (NDVI) were of great importance to grassland fire occurrence. In Inner Mongolia, grassland fires occurring in different areas indicated varying responses to the influencing drivers, and areas that differed in their natural and geographical conditions had different fire-prevention periods. Thus, a grassland fire management strategy based on local conditions should be advocated, and existing fire-monitoring systems based on original meteorological factors should be improved by adding remote sensing data of grassland fuels to increase accuracy.

Accounting for fuel in fire danger forecasts: the fire occurrence probability index (FOPI)

A new fire danger index is proposed to overcome one of the most important limitations of current fire danger metrics. The fire occurrence probability index (FOPI) combines the Canadian fire weather index (FWI) with remote observations of vegetation characteristics to better predict landscape flammability. The FOPI is designed to improve fire danger predictions in all fuel-limited environments where fire is driven by the short-term drying of intermittently-available fuel. The FOPI considerably outperforms the FWI in arid biomes while remaining comparable to the FWI where fuel is abundant.

Іmproving the methodology of natural fire hazard assessment for forested lands based on a risk-oriented approach

An analysis of the combustibility of the forests of «Zhovtneve» State Enterprise was carried out, with the identification of the most fire-prone forests. The distribution of the forest fund plots of each of the researched forestry by classes of natural fire hazard was analyzed. The spatial distribution of the fire density including forest fires as well as wildfires in the 500- meter buffer zone around forest was studied. The article describes a methodology that allows improving the existing methodology for assessing the natural fire hazard of forest lands based on a risk-oriented approach. According to the Kernel density algorithm with the use of desktop GIS systems, a map of the probability of fire occurrence was built. According to the results of the Kernel Density algorithm, the main flammability centers and the density of fires were identified and the territory of the enterprise was divided into classes according to the probability of fires. According to the proposed classification of the fire probability occurrence: low (up to 10.6 cases is the arithmetic average for the entire map); medium (from 10.7 to 28 — 1.01–1.5σ); high (over 28 — over 1.5σ). The integration of the probability of fire occurrence into the scale of natural fire hazard was carried out through the technique of «risk matrices». Thus, the combination of the classes of natural fire hazard classes with the classes of the fire probability occurrence made it possible to obtain an integrated FRS indicator (Fire Risk Score), which includes both: the level of natural fire hazard and the probability of fire occurrence. In order to verify the results, calculations of actual flammability (fire density and flammability by burnt area) were carried out for each class of the national scale of fire hazard and for the FRS classes of each plot. It was found that significant levels of flammability are characteristic only for the first two classes of natural fire hazard (fire density 83.64 cases per 1000 hectares of forests), the flammability of forests with higher natural hazard classes is practically absent, which indicates the need to differentiate the tree stands that are belongs to these two classes. On the other hand, classifying forests with the assignment of points according to FRS revealed an increase in the indicators of forest combustibility and fire density as the FRS indicator increases (at FRS values of 2–3 there are no fires, at FRS values from 4 to 8, combustibility is moderate (from 1.25 to 3.7 cases per 1000 ha), at values above 9 points, the flammability increases sharply and reaches its peak at the FRS value of 13 points (224.5 cases per 1000 ha). Thus, the improved scale based on the risk-oriented approach (fire risk score) more fully reflects the level of danger in the forest based not only on the natural features of the area and fuel complexes characteristics, but also taking into account the spatial location of fire sources and the frequency of fires in each location.

Spatial modelling and mapping of urban fire occurrence in Portugal

Fires in urban areas typically carry severe consequences. High population density together with the complexity of urban network potentially imply significant impacts in property loss, physical damage and life losses. However, despite the impact that fires may have in urban areas, research in urban fire prediction remains limited. In this study, we modelled urban fire occurrences while making a comparative analysis of different strategies to account for spatial autocorrelation. Considering space dependence in addition to a range of social-economic explanatory variables has proven to strengthen the validity of the fitted models.The spatial Durbin error model, including population density, degraded buildings density and buying power, was selected as having the best fit. This model allowed to map the estimated probability of fire occurrence across Portugal, revealing a spatial pattern with clusters centred on the two main Portuguese city districts (Lisboa and Porto). Ultimately, the analysis of the relation between the observed urban fire incidence and the actual number of fire stations in each municipality allowed to underline the need for planning the spatial configuration of fire stations, both in number and location, at a regional scale.

Ongoing climatic change increases the risk of wildfires. Case study: Carpathian spruce forests

The challenge to the sustainable development of forestry in the Eurasian temperate - boreal zone is the increase in the frequency and severity of natural disturbances due to global climate change. In this study, a mathematical model for predicting the risk of wildfires in spruce stands growing in the territory of Slovak Paradise National Park under climate change has been proposed and tested. Wildfire risk is described in terms of the observed probabilities of the destruction of spruce stands in relation to their age for a period of 10 years. As the indicators of assumed climate change, the time series of daily values of four fire weather indices (Angstrőm, Nesterov, Baumgartner, and the Meteorological Forest Fire Risk Index) for the period 1951–2019 have been analysed. The results obtained indicated the significant dependence of the observed increasing annual population proportions of burnt areas on the gradually increasing annual population proportions of risky days recorded and evaluated by using the common scales of risk classification. We found that ongoing climate change has a significant impact on increasing the risk of fires. The Meteorological Forest Fire Risk Index has proven to be the most suitable measure for predicting the probability of fire occurrence under the climate conditions in the experimental territory. The indicated risk of fire occurrence in spruce stands under the assumption of a climatic change is substantially higher than in the case when this assumption is neglected. This information can also serve as a basis for the formulation of efficient landscape fire protection measures focused on building the infrastructure to support the efficient retardation of propagation, including the quick suppression of this detrimental natural hazard.

Effects of forest management practices on carbon dynamics of China's boreal forests under changing climates

Climate change and forest management practices influence forest productivity and carbon budgets, and understanding their interactions is necessary to develop accurate predictions of carbon dynamics as many countries in the world strive towards carbon neutrality. Here, we developed a model-coupling framework to simulate the carbon dynamics of boreal forests in China. The expected dynamics of forest recovery and change following intense timber harvesting in the recent past and projected carbon dynamics into the future under different climate change scenarios and forest management practices (e.g., restoration, afforestation, tending, and fuel management). We predict that under current management strategies, climate change would lead to increased fire frequency and intensity, eventually shifting these forests from carbon sinks towards being carbon sources. This study suggests that future boreal forest management should be altered to reduce the probability of fire occurrence and carbon losses caused by catastrophic fires through planting deciduous species, mechanical removal, and prescribed fire.

Study on the Limit of Moisture Content of Smoldering Humus during Sub-Surface Fires in the Boreal Forests of China

A sub-surface forest fire is a kind of fire that spreads slowly with no flames and lower temperatures, and threatens the ecosystem and human life. The moisture content of humus is considered to be an important factor in determining fire occurrence and sustaining. The humus of the Larix gmelinii in the Daxing’an Mountains was selected for the experiment, the limit moisture content condition of sub-surface forest fires was determined by an experiment simulating smoldering, and the prediction model of the probability of sub-surface forest fire occurrence was established. The results will be of great significance for the prevention, monitoring, and fighting of sub-surface forest fires in the boreal forest. The results showed that when the moisture content of humus in the upper layer was low, the smoldering process could be self-sustaining at 20%. For deeper layers of a depth of 18 cm, this increased to 30% moisture content of the humus and was the critical depth for sub-surface fires. The moisture content of 40% was a limit to burning where smoldering can only last for a short duration and is then extinguished. When the moisture content of the humus was 20%, the smoldering temperature was higher and the rate of spread was faster, with smoldering being maintained for longer periods at 30% moisture content. The regression prediction model of the highest temperature and vertical rate of spread in a column of humus was correlated to moisture content and depth, and the model significance was good at p < 0.01. Based on moisture content and depth, the occurrence probability prediction model of sub-surface fires has a good correlation (R2 = 0.93) and high prediction accuracy (AUC = 0.995). The effect of moisture content (Or = 4.008) on the occurrence probability of sub-surface fires is higher than that of depth (Or = 2.948). The results point out that it is necessary to prevent and monitor the occurrence of sub-surface fires when the humus moisture content is less than 40%. In order to reduce the risk of sub-surface fires, the monitoring time of the fire field should be extended after the fire is extinguished due to the slow-burning process of the sub-surface fire. Increasing the moisture content of the humus is an important method to reduce the probability and restrain the spread of sub-surface fires.

Mapping the probability of wildland fire occurrence in Central America, and identifying the key factors

Background Wildland fires are part of the ecology of forests in Central America. Nevertheless, limited understanding of fire probability and the factors that influence it hinder the planning of intervention strategies. Aims This research combined climatic, anthropogenic and vegetation factors to identify wildland fire probability and determine the most relevant factors. Methods We performed an exploratory analysis to identify important factors and integrated them with fire observations using random forest. We then used the most relevant factors to predict wildland fire occurrence probability and validated our results using different measures. The results demonstrated satisfactory agreement with the independent data. Key results Central regions of Honduras, northern Guatemala and Belize have a very high probability of wildland fire occurrence. Human imprint and extreme climatic conditions influence wildland fire probability in Central America. Conclusions Using random forest, we identified the major influencing factors and areas with a high probability of wildland fire occurence in Central America. Implications Results from this research can support regional organisations in applying enhanced strategies to minimise wildland fires in high-probability areas. Additional efforts may also include using future climate change scenarios and increasing the time frame to evaluate the influence of teleconnection patterns.

Modeling and Mapping of Forest Fire Occurrence in the Lower Silesian Voivodeship of Poland Based on Machine Learning Methods

In recent years, forest fires have become an important issue in Central Europe. To model the probability of the occurrence of forest fires in the Lower Silesian Voivodeship of Poland, historical fire data and several types of predictors were collected or generated, including topographic, vegetation, climatic, and anthropogenic features. The main objectives of this study were to determine the importance of the predictors of forest fire occurrence and to map the probability of forest fire occurrence. The H2O driverless artificial intelligence (DAI) cloud platform was used to model forest fire probability. The gradient boosted machine (GBM) and random forest (RF) methods were applied to assess the probability of forest fire occurrence. Evaluation the importance of the variables was performed using the H2O platform permutation method. The most important variables were the presence of coniferous forest and the distance to agricultural land according to the GBM and RF methods, respectively. Model validation was conducted using receiver operating characteristic (ROC) analysis. The areas under the curve (AUCs) of the ROC plots from the GBM and RF models were 83.3% and 81.3%, respectively. Based on the results obtained, the GBM model can be recommended for the mapping of forest fire occurrence in the study area.

Deterministic–Probabilistic Prediction of Forest Fires from Lightning Activity Taking into Account Aerosol Emissions

Forest fires arise from anthropogenic load and lightning activity. The formation of a thunderstorm front is due to the influence of a number of factors, including the emission of aerosol particles from forest fires. The purpose of this study is mathematical modeling of heat and mass transfer in vegetation firebrand carried out from a forest fire front, taking into account the formation of soot particles to predict forest fire danger from thunderstorm activity. Research objectives: (1) development of a deterministic mathematical model of heat and mass transfer in a pyrolyzed firebrand of vegetation, taking into account soot formation; (2) development of a probabilistic criterion for assessing forest fire danger from thunderstorms, taking into account aerosol emissions; (3) scenario modeling of heat and mass transfer and the formation of a thunderstorm front; (4) and the formulation of conclusions and proposals for the practical application of the developed deterministic–probabilistic approach to the prediction of forest fires from thunderstorms, taking into account aerosol emissions. The novelty of this study lies in the development of a new model of heat and mass transfer in a pyrolyzed vegetation firebrand and a new probabilistic criterion for forest fire danger due to thunderstorm activity, taking into account aerosol emission. The distributions of temperature and volume fractions of phases in a firebrand are obtained for various scenarios. Scenarios of surface fires, crown forest fires, and a fire storm are considered for typical types of coniferous vegetation. Cubic firebrands are considered in the approximation of a two-dimensional mathematical model. To describe the heat and mass transfer in the firebrand structure, a differential heat conduction equation is used with the corresponding initial and boundary conditions, taking into account the kinetic scheme of pyrolysis and soot formation. Variants of using the developed mathematical model and probabilistic criterion in the practice of protecting forests from fires are proposed. Key findings: (1) linear deterministic–probabilistic mathematical model to assess forest fire occurrence probability taking into account aerosol emission and lightning activity; (2) results of mathematical modeling of heat and mass transfer in firebrand taking into account soot formation; (3) and results of scenario modeling of forest fire occurrence probability for different conditions of lightning activity and aerosol emission.

Spatial and Temporal Distribution Characteristics of Active Fires in China Using Remotely Sensed Data

Based on the FIRMS MODIS active fire location data in the Chinese mainland from 2001 to 2018, the GIS fishing net (1 km × 1 km) was used to analyze the spatiotemporal distribution characteristics of active fire occurrence probability and intensity, and a GWLR fire risk assessment model was established to explore its influencing factors. The results show that active fires in the Chinese mainland are mainly low intensity. They are mainly distributed in the area where the annual average temperature is 14–19 °C, the precipitation is 400–800 mm, the surface temperature is 15–20 °C, the altitude is 1000–3000 m, the slope is <15°, and the NDVI value is >0.6. The GWLR fire risk assessment model was constructed to divide mainland China into five fire risk zones. NDVI, temperature, elevation, and slope have significant spatial effects on the occurrence of active fires in the Chinese mainland. Eight fire risk influencing factor areas were divided by calculation, and differentiated fire prevention suggestions are put forward.