Firing Probability Research Articles

Improving a WRF-Based High-Impact Weather Forecast System for a Northern California Power Utility

We describe enhancements to an operational forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to forecast conditions in Northern and Central California for critical decision-making such as proactively de-energizing selected circuits within the power grid. WRF forecasts are routinely produced on a 2 km grid, and the results are used as input to wildfire fuel moisture, fire probability, wildfire spread, and outage probability models. This forecast system produces skillful real-time forecasts while achieving an optimal blend of model resolution and ensemble size appropriate for today’s computational resources afforded to utilities. Numerous experiments were performed with different model settings, grid spacing, and ensemble configuration to develop an operational forecast system optimized for skill and cost. Dry biases were reduced by leveraging a new irrigation scheme, while wind skill was improved through a novel approach involving the selection of Global Ensemble Forecast System (GEFS) members used to drive WRF. We hope that findings in this study can help other utilities (especially those with similar weather impacts) improve their own forecast system.

Coupled Effects of High Temperatures and Droughts on Forest Fires in Northeast China

High temperatures and droughts are two natural disasters that cause forest fires. During climate change, the frequent occurrence of high temperatures, droughts, and their coupled effects significantly increase the forest fire risk. To reveal the seasonal and spatial differences in the coupled effects of high temperatures and droughts on forest fires, this study used the Copula method and proposed the compound extremely high-temperature and drought event index (CTDI). The results indicated that the study area was subject to frequent forest fires in spring (71.56%), and the burned areas were mainly located in forests (40.83%) and the transition zone between farmland and forests (36.91%). The probability of forest fires in summer increased with high temperatures and drought intensity, with high temperatures playing a dominant role. The highest forest fire hazard occurred in summer (>0.98). The probability of a forest fire occurring under extreme meteorological conditions in summer and fall was more than twice as high as that in the same zone under non-extreme conditions. Droughts play a significant role in the occurrence and spread of forest fires during fall. These results can provide decision-making support for forest fire warnings and fire fighting in the Northeast China forest zone.

A Three‐Terminal Memristive Artificial Neuron with Tunable Firing Probability

A Three‐Terminal Memristive Artificial Neuron with Tunable Firing Probability

Interplay of Nav1.8 and Nav1.7 channels drives neuronal hyperexcitability in neuropathic pain.

While voltage-gated sodium channels Nav1.7 and Nav1.8 both contribute to electrogenesis in dorsal root ganglion (DRG) neurons, details of their interactions have remained unexplored. Here, we studied the functional contribution of Nav1.8 in DRG neurons using a dynamic clamp to express Nav1.7L848H, a gain-of-function Nav1.7 mutation that causes inherited erythromelalgia (IEM), a human genetic model of neuropathic pain, and demonstrate a profound functional interaction of Nav1.8 with Nav1.7 close to the threshold for AP generation. At the voltage threshold of -21.9 mV, we observed that Nav1.8 channel open-probability exceeded Nav1.7WT channel open-probability ninefold. Using a kinetic model of Nav1.8, we showed that a reduction of Nav1.8 current by even 25-50% increases rheobase and reduces firing probability in small DRG neurons expressing Nav1.7L848H. Nav1.8 subtraction also reduces the amplitudes of subthreshold membrane potential oscillations in these cells. Our results show that within DRG neurons that express peripheral sodium channel Nav1.7, the Nav1.8 channel amplifies excitability at a broad range of membrane voltages with a predominant effect close to the AP voltage threshold, while Nav1.7 plays a major role at voltages closer to resting membrane potential. Our data show that dynamic-clamp reduction of Nav1.8 conductance by 25-50% can reverse hyperexcitability of DRG neurons expressing a gain-of-function Nav1.7 mutation that causes pain in humans and suggests, more generally, that full inhibition of Nav1.8 may not be required for relief of pain due to DRG neuron hyperexcitability.

Modeling the stability of air flows in inclined workings in case of fire

Purpose. Development of a mathematical model and study of the processes occurring during conveyor belt fires in inclined mines to determine the mechanism of air flow distribution under appropriate conditions. Methods. Practical measurements of the distribution of air flows in an inclined conveyor operated at the mining and processing plant of PJSC ArcelorMittal Kryvyi Rih were carried out. Based on the obtained data, a mathematical model was crea-ted, which was then used to perform Computational Fluid Dynamics (CFD) modeling of gas flows in the mine under normal conditions and in the event of a fire with a thermal capacity of 9 MW. Findings. The study reveals the peculiarities of gas flows during the combustion of a conveyor belt in inclined workings, as well as the influence of turbulence on the flow, changes in the density of gases during heating, and the impact of gravity on the distribution of gases in the cross-section and along the length of the workings. A fire centre with a heat output of 9 MW has a significant impact on the distribution of air flows. The phenomenon of thermal expansion results in a 59.2% increase in the volume of gases behind the fire. The thermal expansion of gases and their low density have a significant impact on the formation of gas flows in inclined workings. As the jet moves away from the centre of the fire, the velocity of the jet gradually increases, reaching a value of 12.4 m/s. This results in a notable alteration in the distribution of total pressure across adjacent workings, accompanied by an increase in flow turbulence. Consequently, the mass of air exiting the right branch is observed to have a five-fold increase in comparison to the pre-fire state. The overturning of the jet from the left branch of the workings gives rise to the exclusive distribution of combustion products along the right branch. Originality. The study helps to understand the mechanism of jet overturning and the peculiarities of the distribution of combustion products in case of fire in inclined conveyor workings. Practical implications. The results of the research allow us to predict the probability of fire, identify the most dangerous areas for fire, and plan the most rational actions for firefighting in specific unique conditions.

SCIENTIFICLY BASED CALCULATION METHODS AND EXPERIMENTAL DATA BASES FOR THE PREVENTION OF PREMIUM FIRE EXPLOSIONS AND UNSAFE EXPLOSIONS OF PYROTECHNIC MULTICOMPONENT NITRATE-METALLIZED MIXTURES UNDER EXTERNAL THERMAL

Problem statement. Methods of preventing the occurrence of forced fire-hazardous destruction of products in the event of exposure to external thermal actions are of great practical importance. At the same time, they should be based on scientifically based methods for determining the critical values of parameters of thermal effects on products and technological parameters of mixture charges, the excess of which leads to premature fire-hazardous destruction of products. To develop such methods, it is necessary to have the results of theoretical and experimental studies of the processes of heating the shells of mixture charges for various external heat flows and their thermoreaction times, as well as the processes of ignition and development of their combustion under different external conditions. Purpose of the article. To form the results of theoretical studies in the form of scientifically based methods that allow preventing and controlling the premature ignition of mixture charges, the explosive development of their combustion process and the fire-hazardous destruction of products under conditions of external thermal effects, as well as the compilation of the results of experimental studies into a single database with determination of the influence of the technological parameters of the mixtures on the characteristics of their ignition processes and the development of combustion, the use of which allows reducing the number of fire-explosive destruction of products in conditions of elevated heating temperatures and external pressures. Conclusion. Scientifically based methods of determining the critical values of the parameters of external thermal actions for the quantitative assessment of the level of fire safety of pyrotechnic products based on multi-component nitrate-metallized mixtures have been developed. A scientific and technical base of experimental data was created to determine the regularities of the influence of a wide class of technological parameters of mixtures on the main characteristics of the processes of ignition and development of combustion of mixtures (ignition temperature, induction time, speed of combustion development) under conditions of external thermal actions (elevated heating temperatures, external pressures, etc.), which makes it possible to determine the probability of fires occurring at facilities where products are stored.

Modeling stand fire probabilities with unobserved heterogeneity. Estimating stand age and climate change effects in Chilean radiata pine plantations

Modeling stand fire probabilities with unobserved heterogeneity. Estimating stand age and climate change effects in Chilean radiata pine plantations

An Assessment of Explosive and Fire Risks from Fighter Jet Collision with Aircraft Carrier

AbstractThis paper presents a framework for evaluating the risk of fire and explosion resulting from potential collisions between fighter jets and aircraft carriers. An event tree is a chronological order of risk issues that may arise from an initial event. This study creates an initial crash scenario based on actual accident cases and risk issues, which are event tree items. Using these scenarios, collision analysis is conducted utilizing finite element analysis to investigate the structural response. It allows us to identify potential fuel leakage and assess whether the collision impact is potent enough to activate the fuze of any onboard weaponry. Consequently, a series of fire analyses pertinent to the respective situations is executed. These analyses aim to discern whether hazardous conditions such as secondary fires or explosions will likely ensue post-collision. By simulating a possible collision scenario between a fighter jet and a flight deck, the proposed method systematically explores and understands the probability and consequences of fire and explosion events following such collisions.

The cerebellum computes frequency dynamics for motions with numerical precision and cross-individual uniformity.

Cross-individual variability is considered the essence of biology, preventing precise mathematical descriptions of biological motion1-7 like the physics law of motion. Here we report that the cerebellum shapes motor kinematics by encoding dynamic motor frequencies with remarkable numerical precision and cross-individual uniformity. Using in-vivo electrophysiology and optogenetics in mice, we confirmed that deep cerebellar neurons encoded frequencies via populational tuning of neuronal firing probabilities, creating cerebellar oscillations and motions with matched frequencies. The mechanism was consistently presented in self-generated rhythmic and non-rhythmic motions triggered by a vibrational platform, or skilled tongue movements of licking in all tested mice with cross-individual uniformity. The precision and uniformity allowed us to engineer complex motor kinematics with designed frequencies. We further validated the frequency-coding function of the human cerebellum using cerebellar electroencephalography recordings and alternating-current stimulation during voluntary tapping tasks. Our findings reveal a cerebellar algorithm for motor kinematics with precision and uniformity, the mathematical foundation for brain-computer interface for motor control.

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.

Predicting forest fire probability in Similipal Biosphere Reserve (India) using Sentinel-2 MSI data and machine learning

The global escalation in forest fires, characterized by increasing frequency and severity, results from a complex interplay of natural and anthropogenic factors, exacerbated by climate change. These fires devastate habitats, threaten species, reduce biodiversity, disrupt natural cycles, and harm local ecosystems. The impacts are particularly damaging in biological reserves. The Similipal Biosphere Reserve (SBR) in Odisha State is one of India’s major forest fire hotspots, experiencing forest fires almost every year. The objective of this study is to develop a predictive model using Sentinel-2 MSI data and machine learning (ML) techniques to estimate the probability of forest fires in the SBR, India, thereby enhancing disaster management and prevention in the region. This research maps and quantifies forest fire intensity by leveraging ML algorithms, namely Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Random Forest (RF). To develop a Forest Fire Probability (FFP) map, twenty conditioning factors, along with pre- and post-fire Normalized Burn Ratio (NBR) and delta Normalized Burn Ratio (dNBR), were utilized. Furthermore, four statistical methods—Mean Absolute Error, Mean Square Error, Root Mean Square Error, and Overall Accuracy—were employed to analyze the FFP. The results were validated using the Area Under Curve (AUC) method. The analysis identifies 2021 as the year with the highest incidence of forest fires, accounting for 29.19% of the occurrences. Among the models, the GBM exhibits superior performance, highlighting its efficacy in handling large, multidimensional datasets. Predictive mapping suggests that approximately 1400–1500 km2, or 25–30% of the studied area, faces a high to very high risk of forest fires.

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.

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.

Eco-friendly MXene/enteromorpha prolifera biomass aerogel with excellent photothermal conversion and flame retardant properties for efficient separation of viscous oil/seawater mixture

Traditional adsorbents are often ineffective in cleaning up high-viscosity oil spills that cause harm to the marine environment. An efficient, safe and eco-friendly solution is urgently needed. Here, we prepare a superhydrophobic MXene/gelatin-enteromorpha prolifera biomass aerogel (H-MXene/G-EP) with excellent photothermal conversion and flame retardancy for solar-driven remediation of high-viscosity oil spills. The short-range aligned multidomain lamellar structure constructed through unidirectional freeze casting endows H-MXene/G-EP with compressibility and an adsorption capacity (63.7 g/g). Due to the excellent light responsiveness and full-spectrum high absorption of H-MXene/G-EP, it can reach a surface temperature 71.6 °C under 1 Sun (1.0 kW/m2), and an in-situ adsorption rate 367.40 g/h for viscous crude oil. The efficiency of separating O/W emulsions is up to 99.1%, which greatly broadens the range of applications for oil/water separation technology. Furthermore, the H-MXene/G-EP exhibits excellent flame retardancy, reducing the heat release rate by 89.59% and the total smoke production rate by 47.83%, respectively. This effectively reduces the probability of secondary marine fires. Thus, this novel biomass-derived aerogel provides a feasible and environmentally friendly solution for effective remediation of marine oil spills.

Part I: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1–10-day range using the U-Net 3+ machine learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model and how input variables are codified for the model. By combining the capabilities of the U-Net 3+ model with a neighborhood loss function, fractions skill score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridded surface meteorological (gridMET) dataset, a daily, CONUS-wide, high-spatial-resolution dataset of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS. Significance Statement Our work seeks to explore the limits to which deep learning can predict wildfire occurrence in CONUS with the ultimate goal of providing decision support to those allocating fire resources during high fire seasons. By exploring with what accuracy and lead time we can provide insights to these persons, we hope to reduce loss of life, reduce damage to property, and improve future event preparedness. We compare two models, one trained on all fires in the continental United States and the other on only large lightning fires. We found that a model trained on all fires produced a higher probability of fire.

Self-gating stochastic-resonance-based autoencoder for unsupervised learning.

Incorporating additive noise components to an ensemble of McCulloch-Pitts neurons can enhance the information representation of the input, asymptotically approaching the average firing probability for large enough ensembles. We further multiply the input by the average firing probability to control the higher probability of self-gating, thereby forming a unified noise-boosted activation model with learnable noise-related hyperparameters. This gating strategy plays a crucial role in improving the performance of neural networks, as evidenced by the optimization of the autoencoder loss at nonzero optimal-noise-scaling hyperparameters, a phenomenon termed self-gating stochastic resonance. Experiments with designed autoencoders using noise-boosted activation functions demonstrate the potential applications of the self-gating stochastic resonance effect in the field of unsupervised learning.

Optimizing Maintenance Strategies for Public Fire Doors in Apartment Buildings: Study on Deterioration Patterns and Replacement Timing

This study aimed to identify factors contributing to the performance degradation of aging fire doors and determine their appropriate replacement time by analyzing the probability of operation of fire doors using building statistics and expert surveys. Aged fire doors with degraded performance should be repaired or replaced quickly; however, there is no clear standard for fire door repair or replacement. Therefore, we analyzed the operation probability of fire doors and conducted expert surveys to determine the proper replacement timing of fire doors and identify the performance degradation factors. The fire door operation probability survey results showed that fire doors operating for those more than 15 years had more than 20% probability of operation failure, and the main causes of operation failure improper installation, insufficient management, or poor performance. The expert survey results indicated that the optimal replacement period for fire doors is 10 to 15 years. The primary factors contributing to performance deterioration were identified as twisting and corrosion (27%), dents (24%), and damage (20%).

Comprehensive risk assessment of flammable refrigerants in HVAC systems based on Bayesian networks and consequence modeling

Promoting environmentally friendly refrigerants is a global imperative. However, their excellent thermal properties often come with flammability risks, raising concerns about HVAC safety. Existing studies focus on basic flammable characteristics and concentration distribution, neglecting comprehensive risk assessment for refrigerant leakage, diffusion, and fire. This paper fills this gap by constructing a comprehensive risk assessment model. The model integrates Bayesian networks to analyze causes of refrigerant leakage and employs CFD to evaluate distribution and residence time of flammable clouds, ignition sources, and consequences of ignition. Validation using real incident events quantitatively assesses risks associated with leaks, flammable cloud migration, ignition probability, and fire hazard. Critical failure points like improper welding and compressor exhaust are identified. Increasing ventilation speed from 0 to 1 m/s and 2 m/s reduces fire probability from 1.65 × 10−9 to 1.0 × 10−9 and 2.5 × 10−10, respectively. Effective measures to reduce leakage and fire risk are proposed based on accident experience and risk acceptability. The model is applicable not only to R290 chillers but also to other vapor compression cycles, aiding safety measures and emergency response plans for accidents involving flammable refrigerants in HVAC systems.

Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization

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

A Global Probability‐Of‐Fire (PoF) Forecast

AbstractAccurate wildfire forecasting can inform regional management and mitigation strategies in advance of fire occurrence. Existing systems typically use fire danger indices to predict landscape flammability, based on meteorological forecasts alone, often using little or no direct information on land surface or vegetation state. Here, we use a vegetation characteristic model, weather forecasts and a data‐driven machine learning approach to construct a global daily ∼9 km resolution Probability of Fire (PoF) model operating at multiple lead times. The PoF model outperforms existing indices, providing accurate forecasts of fire activity up to 10 days in advance, and in some cases up to 30 days. The model can also be used to investigate historical shifts in regional fire patterns. Furthermore, the underlying data driven approach allows PoF to be used for fire attribution, isolating key variables for specific fire events or for looking at the relationships between variables and fire occurrence.