Firing Probability Research Articles

A Substation Fire Early Warning Scheme Based on Multi-Information Fusion

In view of the substation fire early warning using a single information sensor monitoring, it is easy to make mistakes and omissions. Taking the cable in substation as the research object, a multi-information fusion fire prediction model based on back propagation neural network (BPNN) and fuzzy set theory is proposed. Firstly, the BPNN model is trained by using the existing data. Secondly, the artificial fish swarm algorithm (AFSA) is used to optimize the BPNN, which speeds up convergence speed of the model and improves the accuracy of prediction. The fuzzy set theory is applied to fuse the predicted fire probability to obtain the optimal fire prevention and control decision. Finally, the fire protection measures are taken according to the fire decision. The experimental show that the average absolute errors of no fire, smoldering and open fire decreased by 26.06%, 38.5% and 43.13% respectively. The model has higher prediction accuracy, can reasonably output different levels of fire alarm signals, establish substation fire warning and prevention and control system, and provide reference for future substation fire and other disasters warning and prevention and control.

Neuronal hyperexcitability in the Tg2576 mouse model of Alzheimer's disease – the influence of sleep and noradrenergic transmission

The link between Alzheimer's disease (AD) and network hypersynchrony – manifesting as epileptic activities – received considerable attention in the past decade. However, several questions remain unanswered as to its mechanistic underpinnings. Therefore, our objectives were (1) to better characterise epileptic events in the Tg2576 mouse model throughout the sleep-wake cycle and disease progression via electrophysiological recordings and (2) to explore the involvement of noradrenergic transmission in this pathological hypersynchrony. Over and above confirming the previously described early presence and predominance of epileptic events during rapid-eye-movement (REM) sleep, we also show that these events do not worsen with age and are highly phase-locked to the section of the theta cycle during REM sleep where hippocampal pyramidal cells reach their highest firing probability. Finally, we reveal an antiepileptic mechanism of noradrenergic transmission via α1-adrenoreceptors that could explain the intriguing distribution of epileptic events over the sleep-wake cycle in this model, with potential therapeutic implications in the treatment of the epileptic events occurring in many AD patients.

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.

Fire severity and the legacy of mountain pine beetle outbreak: high-severity fire peaks with mixed live and dead vegetation

Bark beetle outbreaks and wildfires are two of the most prevalent disturbances that influence tree mortality, regeneration, and successional trajectories in western North American forests. Subboreal forests have experienced broad overlaps in these disturbances, and recent wildfires have burned through landscapes with substantial tree mortality from prior outbreaks. This study investigated how fuel conditions associated with mountain pine beetle outbreaks influence the probability of high burn severity (i.e. stand-replacing fire) across a range of fire weather conditions in subboreal forests of central interior British Columbia, Canada. We focused on three large fires that occurred in 2012, 2013, and 2014. We characterized outbreak severity, outbreak-influenced prefire vegetation, and subsequent burn severity using Landsat spectral vegetation indices, high-resolution imagery, and field observations. Substantial portions of the prefire landscape contained mixtures of live and dead vegetation created by variable beetle damage and vegetation response—spatial patterns that are related to, but distinct from, peak outbreak severity. We evaluated drivers—fuels, weather, and topography—of high-severity fire under ‘extreme,’ ‘moderate,’ and ‘benign’ fire weather conditions (i.e. burning conditions) using Boosted Regression Trees. While fire weather was a primary driver in most cases, prefire vegetation was an influential predictor variable across all burning conditions, and the probability of high-severity fire was highest when prefire vegetation was a mixture of tree mortality from bark beetles and live vegetation. Thus, while weather and drought are important drivers of wildfires in subboreal forests, bottom-up drivers of elevation and vegetation, including the fuel legacies of bark beetle outbreaks, are crucial factors influencing high-severity burning. The legacy of recent bark beetle outbreaks will continue for decades on these landscapes, affecting fuel structures, future wildfires, forest dynamics, and the broader social-ecological systems of the region.

Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment

The quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it is therefore used to model the complex process of forest fire spread. However, the process of forest fire spread is linked with a variety of mutually influencing factors, which are too complex to analyze using conventional approaches. Here, we propose a new method for modeling fire spread, namely LSSVM-CA, in which least squares support vector machines (LSSVM) is combined with a three-dimensional forest fire CA framework. In this approach, the effects of adjacent wind on the law of fire spread are considered and analyzed. The LSSVM is utilized to derive the complex state transformation rules for fire spread by training with a dataset based on actual local data. To validate the proposed model, the forest fire spread area simulated by LSSVM-CA and the actual extracted forest fire spread area were subjected to cross-comparison. The results show that LSSVM-CA performs well in simulating the spread of forest fire and determining the probability of forest fire.

A Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia

The occurrence and spread of forest fires are the result of the interaction of many factors. In cross-border areas, different fire management systems may lead to different forest fire driving factors. A comparative analysis of the forest fire driving factors in different countries can help to provide ideas for fire prevention and control. In this study, based on the logistic regression (LR) model and standardized coefficients, we determined the relative impact of forest fire driving factors in different countries, in the cross-border area between China, North Korea and Russia from 2001 to 2020, and established a forest fire probability and fire risk level division using a Kriging interpolation. The results show that the climate is the most important factor affecting the probability of forest fires in the cross-border area, followed by the topography and vegetation factors; human activities have the least influence. From a country-by-country perspective, the forest fires on the Chinese side were more affected by humans than on the North Korean and Russian sides and they were mainly concentrated in areas with a low altitude and high population density. The forest fires on the North Korean side and the Russian side were more affected by nature than on the Chinese side and were mainly concentrated in areas with a low altitude, high temperature and little rainfall. The high-risk areas for forest fires were mostly concentrated near the border between China, North Korea and Russia, where transboundary fires pose a great threat to forest resources and rare animals. This study shows that there is a significant difference between the impact of different forest fire management systems on fire conditions, and that active forest fire control policies can effectively reduce the damage caused by forest fires. Due to the complexity of the geopolitics in cross-border areas, transboundary firefighting faces certain difficulties. In the future, it will be necessary to strengthen the cooperation between countries and establish transboundary joint defenses against forest fires in order to protect the declining forest resources and the habitats of rare animals.

Integrating Rangeland Fire Planning and Management: The Scales, Actors, and Processes

Research continually adds to our understanding of the ecological factors and biophysical processes driving frequent, large-scale fires on Great Basin rangelands in the western United States. Yet, even with advances in forecasting rangeland fire probabilities and likely ecological outcomes of fire, it remains difficult for individuals, communities, or organizations to coordinate their actions across jurisdictions and at an ecologically relevant scale to address collective wildfire risk. In this forum, we discuss current institutional arrangements that perpetuate scale mismatches in this system (i.e., institutional objectives, authorities, and capacities that limit coordinated actions to mitigate collective wildfire risk). We make a case for fireshed-scale coordination via rangeland Fireshed Councils, a proposed rangeland and fire planning and management unit that has both biophysical and social relevance to individuals and organizations engaged in fire risk mitigation. A rangeland Fireshed Council offers a venue for diverse group members to mix and match their respective rules and tools to navigate institutional barriers and capacity challenges in new ways. Operating in a collective arrangement at this scale aims to ensure that an individual's or entity's activities transcend traditional modes of planning (i.e., parcel-scale), complement concurrent management activities, and translate to fire-resilient landscapes and human communities. Rangeland Fireshed Councils will require resources and support from high governance levels for sustainability and legitimacy, as well as relative autonomy to determine how best to support local needs.

Getting the news in milliseconds: The role of early novelty detection in active electrosensory exploration

The pulse emitting weakly electric fish Gymnotus omarorum shows stereotyped “novelty responses” consisting of a transient acceleration of the rhythm of a self-emitted electric organ discharge that carries electrosensory signals. Here we show that rapid increases in electric image amplitude cause a “novelty detection potential” in the first electrosensory relay. This sign precedes and its amplitude predicts, the amplitude of the subsequent behavioral novelty response. Current source density analyses indicates its origin ar the layers of the electrosensory lobe where the main output neurons occur. Two types of units, referred to as “ON” and “OFF”. Were recorded there in decerebrated fish. Firing probability of “OFF” units drastically decreased after a stepwise increase in electric image. By contrast, the very first novel stimuli after the increase evoked a sharp peak in firing rate of “ON” units followed by a very fast adaptation phase that contrasted with the slow adaptation observed in previous recordings of primary afferents. The amplitudes of this peak, the novelty detection potential, and the behavioral novelty responses, show the same dependence on the departure of the newest stimulus intensity from the weighted average of preceding ones suggesting that the signals encoded by “ON” neurons underlay the novelty detection potential, propagates through the hierarchical organization of the electromotor control, and finally contribute to accelerate the electric organ discharge rate. This suggests that detecting novelty at the very early processing stage of electrosensory signals is essential to adapt the electrosensory sampling rate to exploration requirements as they change dynamically.

Temperature Regulated Artificial Neuron Based on Memristor

Recently, artificial neurons have attracted much attention due to their excellent energy efficiency and scalability. However, there are still few reports on the regulation of the performance of artificial neuron based on single device. In the letter, we present an artificial neuron device based on Ag/TaOx/Si that not only has excellent turn-on and turn-off performance, but also exhibits sustained stability under multiple cycle tests. Moreover, the Integrate -and-Fire (IF) neuron model is successfully simulated at room temperature. More importantly, we find that an increase of temperature could reduce the threshold voltage and reset voltage of neurons, improve the probability of neuronal firing, and promote the transformation of neurons from nonvolatile to volatile. This work provides an effective method for regulating artificial neurons and has important implications for studying information transfer in biology and adapting complex computations in a memory computing framework.

Study of Building Vulnerabilities to Forest Fires in Portugal

Nowadays with disorderly urban growth into forested areas and climate changes becoming more pronounced, the number of buildings affected by forest fires is increasing. Forest fires are becoming more destructive and socially threatening. However, it is irrefutable that the probability of a forest fire to reach a structure is dramatically reduced by implementing good construction practices and proper fuel management procedures in the environment. Aiming to evaluate the behavior of construction elements of a structure facing a forest fire with testing close to reality, a demonstrative test was performed by the authors in a small house located in a forest environment, in the municipality of Miranda do Corvo, in Portugal. The test consisted of burning a plot of land, with a high forest fuel load, where the house was exposed to a high heat flux originating from the flame front. The separation strip between the fuels and the house was 50cm. With the observation of the results, it was verified that even though the house had an incombustible structure, the construction elements such as window glass, door, thermal insulation and exhaust fans presented vulnerabilities to be considered when it is assumed that a house can serve as shelter in case of fire. It is important to emphasize that the study and research on the vulnerability of buildings in the face of a wildfire at an urban-forest interface must be integrated with all the constraints and variables that surround this theme.

Development of a mathematical model of reliable structures of information-control systems

A mathematical model of the information system (IS) for monitoring the state of objects that may be exposed to extreme influences has been built. The system consists of n devices that work independently of each other. To construct the specified information system, which has the minimum permissible reliability of event recognition, a class of structures of the type «k with n» is considered. Formulas for determining the probabilistic characteristics of n parallel reserved sensors for structures of the «k with n» type were derived; the probability of these events was calculated; and plots of their distribution were constructed. The peculiarity of the derived formulas is that they can be implemented on logical elements with which one can build a physical decision support device. The number of sensors and their corresponding probabilities of correct detection of fire at the given majority values of fire probability were also determined; the cost indicators of the information system were defined. A method for improving the reliability of IS has been developed, based on the use of the optimal number of information sensor. The ratio of finding probabilistic states of IS for structures of the «k with n» type was obtained. Algorithms for calculating the probabilities of IS states, as well as an algorithm for determining the number of information sensors and the corresponding probabilities of fire detection, have been developed. The feature of these algorithms is that they make it possible to determine the optimal number of information sensors. An estimate of the effectiveness of IS indicators of the considered types of structures was found: the probability of correct detection, the probability of non-detection, and false alarm. The reported results can be used to select the optimal structure for recognizing dangerous flight situations: choosing the number of sensors corresponding to the high probability of correct detection and the minimum probabilities of non-detection and false alarms, taking into consideration the cost of sensors.

Modeling the spatial evolution wildfires using random spread process

AbstractThe study of wildfire spread and the growth of the area burned is an important task in ecological studies and in other contexts. In this work we present a model for fire spread and show the results obtained from simulations of burned areas. The model is based on probabilities of fire at different locations. Such probabilities are obtained from the intensity function of a spatial point process model fitted to the observed pattern of fires in the Valencian Community for the years 1993–2015. The models, applied to different wildfires in Spain, including the different temporal states, combines the features of a network model with those of a quasi‐physical model of the interaction between burning and nonburning cells, which strongly depends on covariates. The results of the simulated wildfire burned areas resemble the burned areas observed in real cases, suggesting that the model proposed, based on a Markov process called Random Spread Process, works adequately. The model can be extended to simulate other random spread processes such as epidemics.

Design and Optimization for a New Locomotive Power Battery Box

To solve the disadvantages of the low protection grade, high weight, and high cost of the existing locomotive power battery system, this study optimizes the existing scheme and introduces the design concept of two-stage protection. The purpose of the research is to improve the protection level of the battery pack to IP68, to optimize the sheet metal power battery box structure into a more lightweight frame structure, to simplify the cooling mode of the battery pack for natural air cooling, and to improve the battery protection level and maintain the heat exchange capability. In the course of the study, a design scheme with a two-stage protection function is proposed. The numerical model analyzes the self-load, transverse load, longitudinal load, mode, and fatigue, and optimizes the layout of the power tank cell. The optimized box model was physically tested and economically compared. The results show that: (1) The maximum load stress is 128.4 MPa, which is lower than 235 MPa, the ultimate stress of the box material, and the fatigue factor of the frame box structure is 3.75, which is higher than 1.0, and it is not prone to fatigue damage. (2) Under the low-temperature heating condition, the overall temperature rise of the battery pack is 4.3 °C, which is greater than 2.3 °C under the air conditioning heat dissipation scheme. Under the high-temperature charging condition, the overall temperature rise of the battery pack is 2.0 °C, and the temperature value is the same as the temperature rise under the air conditioning cooling scheme. Under the high-temperature discharge condition, the overall temperature rise of the battery pack is 3.0 °C, and the temperature value is greater than 2.1 °C under the air conditioning heat dissipation scheme. At the same time, the temperature rise under the three working conditions is less than the 15 °C stipulated in the JS175-201805 standard. The simulation results show that the natural airflow and two-stage protection structure can provide a good temperature environment for the power battery to work. (3) The optimized box prototype can effectively maintain the structural integrity of the battery cell in the box in extreme test cases, reducing the probability of battery fire caused by battery cell deformation. (4) The power battery adopts a two-stage protection design under the battery power level, which can simultaneously achieve battery protection and prevent thermal runaway, while reducing costs. The research results provide a new concept for the design of a locomotive power battery system. (5) The weight of the optimized scheme is 2020 kg, and the original scheme is 2470 kg; thus, the reduction in weight is 450 kg. Meanwhile, the volume of the optimized scheme is 1.49 m3, and the original scheme is 1.93 m3; thus, the reduction in volume is 0.44 m3.

Task Allocation and Traffic Route Optimization in Hybrid Fire-fighting Unmanned Aerial Vehicle Network

With the increase of extreme weather conditions in the world, the probability of forest fires is increasing. How the forest fire management decision-making system can monitor and control the fire quickly and effectively is the key of forest fire fighting work. This paper uses SSA drones carrying high-definition and thermal imaging cameras and telemetry sensors in conjunction, as well as Repeater drones used to greatly expand the frontline low-power radio range, to support fire management decision-making systems. At the same time, explore a drone cooperation plan to deal with different fire terrains and different scales of fire conditions. The aim of this paper is to improve the existing fire management decision system in order to quickly respond to the emergency fire. Research object for the Australian state of Victoria on October 1, 2019 to January 7, 2020 wildfires, explore SSA drones and Repeater drones in the application of the forest fire, ensure that fire management decision-making system to provide the optimal number deployment scheme of fire task quickly and efficiently, and achieve the maximum efficiency and economic optimal compatibility.

Wildfire, protected areas and forest ownership: The case of China

Protected areas (PAs), which are a major tool for conserving land cover and ecosystem services, are expected to restore natural fire regimes and allow better fire management methods. However, little is known about the relationship between protected area (PA) establishment and fire risk. Using field plots in the National Forest Inventory (NFI) of China, this study is designed to assess the association of PA establishments and fire risk. We find that PA establishments have been significantly related with the decrease of the fire occurrence probability. We also find that forest ownership is a key factor influencing fire activities within protected areas. Inside protected areas, privately or collectively owned plots have a much higher fire occurrence probability than state-owned plots. Empirical evidences shows that inadequate compensation from protects areas of China is one possible reason for the higher fire risk in collective or individual owned forests. As a solution, more financial compensation within PAs is urgently needed in China.

Where There's Smoke, There's Fuel: Dynamic Vegetation Data Improve Predictions of Wildfire Hazard in the Great Basin

Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, nonforested ecosystems is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32-yr time series of spatial data, we employed machine learning algorithms to predict the relative probability of large (> 405 ha) wildfire in the Great Basin based on fine-scale annual and 16-d estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluated the predictive utility of these models with a leave-1-yr-out cross-validation, building spatial hindcasts of fire probability for each year that we compared against actual footprints of large wildfires. Herbaceous aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of burning. Across 32 fire seasons, 88% of the area burned in large wildfires coincided with the upper 3 deciles of predicted fire probabilities. At the scale of the Great Basin, several metrics of fire activity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather-driven growth of fine fuels and reveal a significant increasing trend in fire probability over the past 3 decades driven by widespread changes in fine fuel characteristics.

Air quality forecasting using artificial intelligence techniques

Air pollution is among the most challenging issues modern societies are currently facing with direct impacts on society, economy, politics, as well as on the health of the population and the environment (European Environmental Agency, 2020). This study aims to introduce a state-of-the-art air quality forecasting model using a Deep Long-Short Memory Network integrating data from various sources, from in-situ measurements coming from official ground-based stations to Earth Observation from the Copernicus Atmosphere Monitoring Service (CAMS). The overall aim for the development of this model is the production of air quality forecasts with higher spatial and temporal resolution than those offered from Copernicus, capturing air pollution dynamics at a city level, something that will significantly affect decision-making allowing the adoption and implementation of better measures by policy makers to minimise the effects of the degraded air quality. The air quality forecasting model presented in this study uses a novel method based on artificial neural networks in order to produce forecasts of future air quality conditions with high spatial and temporal resolution, by leveraging data from a wide range of sources. Even though it was developed in the context of addressing the challenging issue of air pollution, the model presented in this study could also be used, with a few modifications, to produce forecasts of thermal comfort and the probability of forest fires. Keywords: air pollution, artificial intelligence, artificial neural networks, forecast

Using Dynamic, Fuels-Based Fire Probability Maps to Reduce Large Wildfires in the Great Basin

Spatial and temporal dynamics of rangeland fuels is a primary factor driving large wildfires. Yet detailed information capturing variation in fine fuels has largely been missing from rangeland fire planning and fuels management. New fuels-based maps of Great Basin rangeland fire probability help bridge this gap by coupling dynamic vegetation cover and production data from the Rangeland Analysis Platform with weather and climate data to provide annual forecasts of the relative probability of large wildfire. In this paper, we review these new fuels-based maps and discuss implications for prefire planning, preparedness, and strategic fuels management. Examining patterns of fire probability through time reveals high spatial and temporal variation in risk of large wildfires across the Great Basin. Certain areas are chronically impacted with high fire probability most years, while others have more sporadic or low probability of large fire annually. Maps confirm previous research that the recent increase in large fire risk in the region is highly associated with invasive annual grasses, but total aboveground herbaceous production (including perennials) continues to be a primary predictor of fire probability. Fuels-based fire probability maps can be used alongside existing data sources and prioritization frameworks by fire and fuels managers to inform questions of 1) what kind of fire year might this be, 2) where large fires are most likely to occur given an ignition, and 3) where resources should be focused. We provide examples of how maps can be used to improve prefire preparedness and planning to enhance suppression, facilitate annual targeting of fine fuels reductions, and support land use planning for implementation of landscape-scale fuels management. Proactively incorporating this new information into rangeland fire and fuels management can help address altered fire regimes threatening the region's wildlife and working lands.

Identifying fire safety in hospitals: Evidence from Changsha, China

Since the outbreak of the COVID-19 pandemic, fires occurred frequently in hospitals managing COVID-19, and caused over 279 deaths. Fire safety in hospitals should be identified clearly and taken seriously. Fire probability and fire service coverage for hospitals from a national perspective in China were analyzed in this study. Calculated with the generalized Barrois model, the annual fire frequency of hospital building exceeds 0.5, when its floor area reaches approximately 180,000 m2. Based on the number of hospital fires in Changsha and that of hospitals in China from 2014 to 2017, the average annual fire probability of a hospital in China was calculated to be 0.017. The total effective coverage rate (TECR) of fire service for hospitals in Changsha, China was estimated to be between 11.82 % and 25.74 %, based on real-time travel times extracted from the Baidu Map. The TECR of national fire service for hospitals was estimated to be between 14.18 % and 30.89 %, according to the ratio of the number of hospitals and the number of fire stations in China to that in Changsha. Currently, recruiting medical staff as fire volunteers can quickly improve fire safety in hospitals for a low cost.

Contribution of anthropogenic, vegetation, and topographic features to forest fire occurrence in Poland

Climate is one of the main causes of forest fires in Europe. In addition, forest fires are influenced by other factors, such as the reconstruction of tree stands with a uniform species composition and increasing human pressure. At the same time, the increasing number of fires is accompanied by a steady increase in the number and quality of spatial information collected, which affects the ability to conduct more accurate studies of forest fires. The appropriate use of spatial information systems (GIS) together with all the collected information on fires could provide new insights into their causes and, in further steps, allow the development of new, more accurate predictive models. The objectives of the study were: (i) to estimate the probability of fire occurrence in the period 2007-2016; (ii) to evaluate the performance of the developed model; (iii) to identify and quantify anthropogenic, topographic and stand factors affecting the probability of fire occurrence in forest areas in Poland. To achieve these objectives, a statistical model based on a logistic regression approach was built using the nationwide forest fire database for the period from 2007 to 2016. The information in the database was obtained from the Polish State Forest Information System (SILP). Then it was supplemented with spatial, topographic and socio-economic information from various spatial and statistical databases. The results showed that fire probability is significantly positively affected by population density and distance from buildings. In addition, the further the distance from roads and railways, watercourses and water objects or the edge of the forest, height above sea level, and steep slopes, the lower is the fire probability. Analysis of spatial, ecological and socio-economic factors provides new insights that contribute to a better understanding of fire occurrence in Poland.