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Human ability to manipulate fire and the landscape has increased over evolutionary time, but the impact of this on fire regimes and consequences for biodiversity and biogeochemistry are hotly debated. Reconstructing historical changes in human-derived fire regimes empirically is challenging, but information is available on the timing of key human innovations and on current human impacts on fire; here we incorporate this knowledge into a spatially explicit fire propagation model. We explore how changes in population density, the ability to create fire, and the expansion of agropastoralism altered the extent and seasonal distribution of fire as modern humans arose and spread through Africa. Much emphasis has been placed on the positive effect of population density on ignition frequency, but our model suggests this is less important than changes in fire spread and connectivity that would have occurred as humans learned to light fires in the dry season and to transform the landscape through grazing and cultivation. Different landscapes show different limitations; we show that substantial human impacts on burned area would only have started ∼4,000 B.P. in open landscapes, whereas they could have altered fire regimes in closed/dissected landscapes by ∼40,000 B.P. Dry season fires have been the norm for the past 200–300 ky across all landscapes. The annual area burned in Africa probably peaked between 4 and 40 kya. These results agree with recent paleocarbon studies that suggest that the biomass burned today is less than in the recent past in subtropical countries.

The suggestion has been made within the wildland fire community that the rate of spread in the upper portion of the fire danger spectrum is largely independent of the physical fuel characteristics in certain forest ecosystem types. Our review and analysis of the relevant scientific literature on the subject suggest that fuel characteristics have a gradual diminishing effect on the rate of fire spread in forest and shrubland fuel types with increasing fire danger, with the effect not being observable under extreme fire danger conditions. Empirical-based fire spread models with multiplicative fuel functions generally do not capture this effect adequately. The implications of this outcome on fire spread modelling and fuels management are discussed.

BackgroundWildfire often shows complex dynamic behaviour due to the inherent nature of ambient conditions, vegetation and ignition patterns. Merging fire is one such dynamic behaviour that plays a critical role in the safety of structures and firefighters.Aim & methodThe aim of this study was to develop better insight and understanding of the interaction of parallel merging firelines, using a numerical validation of a physics-based CFD wildfire model concerning merging fires.ConclusionsThe validated model shows a relative error of 5–35% in estimating the rate of fire spread compared with the experimental observation in most of the cases. A physical interpretation is presented to show how parallel fire behaves and interacts with the ambient conditions, providing complementary information to the experimental study.ImplicationsThe validated numerical model serves as a base case for further study in developing a better correlation for the rate of fire spread between parallel firelines with different ambient conditions, especially at the field scale.

Modeling forest fire spread is a very complex problem, and the existing models usually need some input parameters which are hard to get. How to predict the time series of forest fire spread rate based on passed series may be a key problem to break through the current technical bottleneck. In the process of forest fire spreading, spread rate and wind speed would affect each other. In this paper, three kinds of network models based on Long Short-Term Memory (LSTM) are designed to predict fire spread rate, exploring the interaction between fire and wind. In order to train these LSTM-based models and validate their effectiveness of prediction, several outdoor combustion experiments are designed and carried out. Process data sets of forest fire spreading are collected with an infrared camera mounted on a UAV, and wind data sets are recorded using a anemometer simultaneously. According to the close relationship between wind and fire, three progressive LSTM based models are constructed, which are called CSG-LSTM, MDG-LSTM and FNU-LSTM, respectively. A Cross-Entropy Loss equation is employed to measure the model training quality, and then prediction accuracy is computed and analyzed by comparing with the true fire spread rate and wind speed. According to the performance of training and prediction stage, FNU-LSTM is determined as the best model for the general case. The advantage of FNU-LSTM is further demonstrated by doing comparison experiments with the normal LSTM and other LSTM based models which predict both fire spread rate and wind speed separately. The experiment has also demonstrated the ability of the model to the real fire prediction on the basis of two historical wildland fires.

There are three main types of forest fires: surface fires, tree crown fires, and underground fires. The frequency of surface fires and tree crown fires accounts for more than 90% of the overall frequency of forest fires. In order to construct an immersive three-dimensional visualization simulation of forest fires, various forest fire ignition methods, forest fire spread, and fire extinguishing simulation exercises are studied. This paper proposes a lightweight forest fire spread method based on cellular automata applied to the virtual 3D world. By building a plant model library using cells to express different plants, and by building a 3D geometric model of plants to truly capture the combustion process of a single plant, we can further simulate forest-scale fire propagation and analyze the factors that affect forest fire spread. In addition, based on the constructed immersive forest scene, this study explored various forms of fire extinguishing methods in the virtual environment, mainly liquid flame retardants such as water guns, helicopter-dropped flame retardants, or simulated rainfall. Therefore, the forest fire occurrence, spread, and fire extinguishing process can be visualized after the interactive simulation is designed and implemented. Finally, this study greatly enhanced the immersion and realism of the 3D forest fire scene by simulating the changes in plant materials during the spread of a forest fire.

With the development of computer technology, forest fire spread simulation using computers has gradually developed. According to the existing research on forest fire spread, the models established in various countries have typical regional characteristics. A fire spread model established in a specific region is only suitable for the local area, and there is still a great deal of uncertainty as to whether or not the established model is suitable for fire spread simulation for the same fuel in other regions. Although many fire spread models have been established, the fuel characteristics applicable to each model, such as the fuel loading, fuel moisture content, combustibility, etc., are not similar. It is necessary to evaluate the applicability of different fuel characteristics to different fire spread models. We combined ground investigation, historical data collection, model improvements, and statistical analysis to establish a multi-model forest fire spread simulation method (FIRER) that shows the burning time, perimeter, burning area, overlap area, and spread rate of fire sites. This method is a large-scale, high-resolution fire growth model based on fire spread in eight directions on a regular 30 m grid. This method could use any one of four different physical models (McArthur, Rothermel, FBP, and Wang Zhengfei (China)) for fire behavior. This method has an option to represent fire breaks from roads, rivers, and fire suppression. We can evaluate which model is more suitable in a specific area. This method was tested on a single historical lightning fire in the Daxing’an Mountains. Different scenarios were tested and compared: using each of the four fire behavior models, with fire breaks on or off, and with a single or suspected double fire ignition location of the historical fire. The results show that the Rothermel model is the best model in the simulation of the Hanma lightning fire; the overlap area is 5694.4 hm2. Meanwhile, the real fire area in FIRER is 5800.9 hm2; both the Kappa and Sørensen values exceed 0.8, providing high accuracy in fire spread simulations. FIRER performs well in the automatic identification of fire break zones and multiple ignited points. Compared with FARSITE, FIRER performs well in predicting accuracy. Compared with BehavePlus, FIRER also has advantages in simulating large-scale fire spread. However, the complex data preparation stage of FIRER means that FIRER still has great room for improvement. This research provides a practical basis for the comparison of the practicability and applicability of various fire spread models and provides more effective practical tools and a scientific basis for decision-making and the management of fighting forest fires.

The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

Mountains are popular tourist destinations due to their climate, fresh atmosphere, breathtaking sceneries, and varied topography. However, they are at times exposed to accidents, such as fire caused due to natural hazards and human activities. Such unforeseen fire accidents have a social, economic, and environmental impact on mountain towns worldwide. Protecting mountains from such fire accidents is also very challenging in terms of the high cost of fire containment resources, tracking fire spread, and evacuating the people at risk. This paper aims to fill this gap and proposes a three-fold methodology for fire safety in the mountains. The first part of the methodology is an optimization model for effective fire containment resource utilization. The second part of the methodology is a novel ensemble model based on machine learning, the heuristic approach, and principal component regression for predictive analytics of fire spread data. The final part of the methodology consists of an Internet of Things-based task orchestration approach to notify fire safety information to safety authorities. The proposed three-fold fire safety approach provides in-time information to safety authorities for making on-time decisions to minimize the damage caused by mountain fire with minimum containment cost. The performance of optimization models is evaluated in terms of execution time and cost. The particle swarm optimization-based model performs better in terms of cost, whereas the bat algorithm performs better in terms of execution time. The prediction models’ performance is evaluated in terms of root mean square error, mean absolute error, and mean absolute percentage error. The proposed ensemble-based prediction model accuracy for fire spread and burned area prediction is higher than that of the state-of-the-art algorithms. It is evident from the results that the proposed fire safety mechanism is a step towards efficient mountain fire safety management.

This paper experimentally evaluates the effect of slope on spread of a linear flame front over a pine needle fuel bed in still air. The slope angle of the fuel bed varied from 0 to 32°. The fuel mass consumption in flaming fire spread, temperature over the fuel bed, velocities of the flow around the flame front and heat fluxes (total and radiant) near the end of the fuel bed were measured. The mass loss rate and rate of fire spread both increased with increasing slope, whereas the fuel consumption efficiency varied in the opposite way. It was shown that a weak reverse inflow and an upslope wind (induced by the flame itself) exist respectively ahead of and behind the flame front, and their significant difference in velocity (causing a pressure difference) plays an essential role in the forward tilting of the flame front. This mechanism promotes burning, especially on higher slopes. Natural convective cooling has a remarkable effect on the fuel pre-heating in the spread of linear flame fronts under slope conditions. A fire spread model for a linear flame front was developed to consider the natural convective cooling and the fuel consumption efficiency. The model agrees well with the experimental data on fire spread rate. Its reliability, especially for higher slopes, was verified by comparison with other models.

The popular simulation process that uses traditional cellular automata with a fixed time step to simulate forest fire spread may be limited in its ability to reflect the characteristics of actual fire development. This study combines cellular automata with an existing forest fire model to construct an improved forest fire spread model, which calculates a speed change rate index based on the meteorological factors that affect the spread of forest fires and the actual environment of the current location of the spread. The proposed model can adaptively adjust the time step of cellular automata through the speed change rate index, simulating forest fire spread more in line with the actual fire development trends while ensuring accuracy. When used to analyze a forest fire that occurred in Mianning County, Liangshan Prefecture, Sichuan Province in 2020, our model exhibited simulation accuracy of 96.9%, and kappa coefficient of 0.6214. The simulated fire situation adapted well to the complex and dynamic fire environment, accurately depicting the detailed fire situation. The algorithm can be used to simulate and predict the spread of forest fires, ensuring the accuracy of spread simulation and helping decision makers formulate reasonable plans.