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With the advent of modern technologies toward motoring the vehicles with connectivity of vehicle networks, the entire electrical system of the vehicle is becoming more and more complex. Also, the sales of vehicles in all categories are growing at a very high rate across the globe. With this heavy loading of electrical complexity, nowadays vehicles are becoming more vulnerable to thermal incidences. A vehicle fire is a very serious safety incident, which may lead to loss of human life, results in property loss, business discontinuity and substantial public cost. Vehicle fire incidents encourage manufacturers to take proactive measures to ensure “no fire incident” on the vehicle. The paper explains how the team devised a systematic approach to pre-empt all potential thermal risks at the early stages of vehicle design and eliminate all the thermal concerns by implementing the preventive measures. Fire path analysis evaluates vulnerable components on the vehicle that may cause thermal incidents, how fire can propagate from the initial source to the last carriers and how to break the fire propagation path from source to the next carrier components on the vehicle. It has been implemented fire path analysis on passenger cars, commercial vehicles, buses and electric vehicles. It has been seen a 91% drop in thermal incidents in productionised vehicles and ensured flawless launching of the vehicle; hence fire path analysis is a smart tool to prevent vehicle fire incidents at early stages of the design to safeguard precious human life.

Fire simulators allow predicting fire spread and behavior and some of which in real-time. Both strategies and tactics to suppress wildland fires depend on fire analysis which is generally based on fire simulations that need to be accurate for a proper decision making. However, limitations and assumptions of the fire models and uncertainties in input data may induce errors in the simulated fire growth and, therefore, fire simulations do not always match actual fire spread. In this paper, we show an innovative method implemented in Wildfire Analyst to adjust fire simulations in real-time. The method determines the adjustment factors needed for the optimal rate of spread by fuel model in order to minimize the arrival time error between the simulated fire and a set of control points where the arrival time of the observed (real) fire is known. Two case studies were used to present the method, showing robust results in reducing the error and fitting the simulated fire growth to the real fire spread, with practical real-time implications. The method presented may be solved in real-time and used with any empirical fire propagation.

Forest fire causes significant damages to environment and living organisms. It becomes troublesome to recover from the consequences of forest fire. The early and accurate prediction of forest fire propagation in forest area will help in fire management efforts. The models required for prediction of forest fire spread requires many parameters and which are not known precisely during the emergency. So there is a need of intelligent system to model and predict the propagation of forest fire to reduce the consequences of forest fire and minimize the management efforts. In this work, we have discussed different techniques available in literature for forest fire prediction. This paper also describes the implementation and analysis of prediction of forest fire propagation using Genetic Algorithm and Geometric Semantic Genetic Programming.

Forest fires provoke significant loses from the ecological, social and economical point of view. Furthermore, the climate emergency will also increase the occurrence of such disasters. In this context, forest fire propagation prediction is a key tool to fight against these natural hazards efficiently and mitigate the damages. However, forest fire spread simulators require a set of input parameters that, in many cases, cannot be measured and must be estimated indirectly introducing uncertainty in forest fire propagation predictions. One of such parameters is the wind. It is possible to measure wind using meteorological stations and it is also possible to predict wind using meteorological models such as WRF. However, wind components are highly affected by the terrain topography introducing a large degree of uncertainty in forest fire spread predictions. Therefore, it is necessary to introduce wind field models that estimate wind speed and direction at very high resolution to reduce such uncertainty. Such models are time consuming models that are usually executed under strict time constrains. So, it is critical to minimize the execution time, taking into account the fact that in many cases it is not possible to execute the model on a supercomputer, but must be executed on commodity hardware available on the field or at control centers. This work introduces a new parallelization approach for wind field calculation based on Python multiprocessing to accelerate wind field evaluation. The results show that the new approach reduces execution time using a single personal computer.

Forests are a natural renewable resource and can meet the needs of the society, provided that they are used for a multiple, rational, continuous and sustainable use. Forest fires are a natural component of forest ecosystems and cannot be completely eliminated. However, in recent decades, there has been a tendency to transform forest fires from a natural regulatory factor into a catastrophic phenomenon causing significant economic, environmental and social damage. It is critical to understand the relationships between the underlying environmental factors and spatial behaviour of a forest fire in order to develop effective and scientifically sound forest fire management plans. The key objective of this study is to enhance the efficiency of the formation of a real-time forest fire forecast under the unsteady and uncertain conditions. In the article, the author proposes to develop an intelligent system for predicting the forest fire development based on artificial intelligence and deep computer-aided learning. A key element of the system is forest fire propagation models that recognise data from successive images, predict the forest fire dynamics and generate an image with a fire propagation forecast. It is proposed to build forest fire propagation models by using a real-time forest fire forecasting method. In the article, the author presented a structural diagram of an intelligent system to forecast the dynamics of a forest fire and described the functional structure of the system by constructing its functional models in the form of IDEF0 diagrams.

Knock-on effects or so-called domino effects in the process industries may cause much greater losses than merely a primary event. Probability analysis of accidents resulting from domino effects is important for risk assessment. However, for the accident occurrence of a unit there may be mutual influences between the units in the area influenced by the accidents due to a domino effect, and this makes the calculation of probabilities of the accidents rather difficult. A matrix-based approach is proposed to model the influences between units influenced by a fire-induced domino effects, and the analysis approach for accident propagation as well as a simulation-based algorithm for probability calculation of accidents is provided. The synergistic effect of thermal radiation is taken into account during the accident propagation. The proposed approach is flexible to model and analyze domino effects in various conditions of primary fires by only changing the value of the initial matrix indicating the fire states. Two examples illustrate analyzing the fire propagation among tanks storing flammable liquids. The results show that this approach is simple but effective for offering an insight in the accident propagation process and for knowing the probabilities of equipment getting on fire.

Coupled CFD fire simulations and thermomechanical FE analyses typically consist of fire simulations, heat transfer analyses and structural response analyses, mutually coupled by three coupling steps. There are two coupling approaches, one-way and two-way coupling, where two-way coupling includes the effects of the structural response on fire propagation. In the first part of this paper, one- and two-way coupling approaches including the coupling steps are proposed to include coupling at the structural level. Then a case study comprising an office space with a 12-plate thin-walled steel façade under fire conditions is introduced, as well as the related CFD and FE models. A newly developed automated coupling interface and subprograms are used to perform several one-way and two-way coupled analyses using a coarse and fine CFD mesh for the case study. Slight differences are found in the results of identical simulations due to random effects in the fire simulations. Nevertheless, it can be concluded that two-way coupling is feasible, and that significant differences in the façade failure progression illustrate its effectiveness. Future research includes additional developments of both the fire and structural models, as well as verification and parametric studies to further confirm the findings.

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s)

Every year, thousands of forest hectares are burned worldwide, causing important consequences on the atmosphere, biodiversity and economy. Proper prediction of the fire evolution allows to manage the fire fighting equipment properly. Therefore, it is crucial to use reliable and fast simulations in order to predict the evolution of the fire. WRF-SFIRE is a wildland fire simulator, which couples a meteorological model called Weather Research and Forecasting Model (WRF) and a forest fire simulator, SFIRE reproducing the interaction between the propagation of the fire and the atmosphere evolution. The mesh resolution used to solve the atmosphere evolution has a deep impact in the prediction of small scale meteorological effects. At the same time, the ability of introducing these small scale meteorological events into the forest fire simulation implies enhancements in the quality of the data that drives the simulation. Therefore, better fire propagation predictions are obtained. However, this improvement can be affected by the instability of the problem to be solved. This paper states an accuracy study changing the mesh resolution when running WRF-SFIRE using as example a real case that took place in Catalonia (northeast of Spain) in 2005.

Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) to predict the fire propagation. However, these parameters are usually difficult to measure or estimate precisely, and there is a high degree of uncertainty in many of them. This uncertainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.

A Kalman filter-based wind estimation method is presented in this paper for wildfire monitoring. Wind behavior is one of the key factors in fire propagation, especially on the early stage of a fire. This quadrotor UAV-based method is proposed to estimate the lateral wind in the fire environment only with a standard autopilot sensor suite on board. A Kalman filter is designed to estimate the wind vector based on the vehicle’s hovering inclined angle in the wind environment. The designed Kalman filter can reduce the influence of sensor noises and improve the accuracy of the wind estimation. The effectiveness of the algorithm is demonstrated in a simulation environment.

In this article we are going to present the optimizations that has been done through different types of modeling actions on wildland fires for Bulgarian test cases. We will present approaches where meteorological data along with terrain specific relief and vegetation coverage are modeled in a way to present credible scenarios for wildland propagation used for calibration purposes of the different approaches. This work aims to prove that the used modeling tools can be used also in real time decision support for the responsible authorities when it comes to wildland fire propagation and the measures corresponding to limitation of its devastating consequences for the nature and human lives.

Predicting forest fire propagation is a crucial issue to mitigate fire damages.Wind significantly affects forest fire propagation.Wind field calculation implies solving extremely large systems of equations.The Schur method has been applied to accelerate wind field calculation.Execution time is reduced to accomplish operational requirements. Forest fire are natural hazards that every year cause significant looses. Predicting the evolution of a forest fire is a critical issue in mitigating its effects. Such predictions must accomplish strict real time constraints to be effective. Wind field calculation is a key issue in providing accurate forest fire propagation predictions. However, it implies solving large linear systems with 105 to 108 variables that takes too long using conventional methods. Therefore, the domain decomposition Schur method has been applied to accelerate wind field calculation. Using the Schur method, the linear system is significantly reduced and several phases can be parallelised exploiting cluster computing capabilities. Results show that the execution time for the wind field calculation of a map of 800 × 800 cells has been reduced from 400 s to 90 s using 10 nodes.

The accurate prediction of forest fire propagation is a crucial issue to minimize its effects. So, several models have been developed to determine the forest fire propagation beforehand. Such models require several input parameters that, in some cases, cannot be known precisely in a real emergency. So, a Two-Stage methodology was developed to calibrate the input parameters to improve the quality of the prediction. This methodology was based on Genetic Algorithms which require the execution of many simulations. Moreover, when the fire is large some input parameters cannot be considered uniform among the whole fire and extra models must be introduced. One of these non-uniform parameters is wind. So, in this work a wind field model is introduced. This model implies more computation time and response time is the main constraint. The prediction must be provided as fast as possible to be useful, thus it is necessary to exploit all available computing resources. So a Hybrid MPI-OpenMP application has been developed to reach a response in the shortest possible time. This work focuses on reducing the execution time of a worker in a MPI Master/Worker structure analyzing the simulation software parts which compose the Fire Simulator System.

In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate measure of confidence in their performance. There are different ways and different methodologies to establish and measure the confidence of the models. In this paper, we focus on the forest fire spread prediction. Simulators implementing forest fire spread models require diverse input parameters to deliver predictions about fire propagation. However, the data describing the actual scenario where the fire is taking place are usually subject to high levels of uncertainty. In order to minimize the impact of the input-data uncertainty a Two-Stage methodology was developed to calibrate the input parameters in (1) an adjustment stage so that the calibrated parameters are used, and (2) the prediction stage to improve the quality of the predictions. Is in the adjustment stage where the error formula plays a crucial role, because different formulas implies different adjustments and, in consequence, different wild fire spread predictions. In this paper, different error functions are compared to show the impact in terms of prediction quality in DDDAS for forest fire spread prediction. These formulas have been tested using a real forest fire that took place in Arkadia (Greece) in 2011.

The objective of this work is to illustrate how to algorithmically integrate Machine-Learning Algorithms (MLA’s) with multistage/multicomponent fire spread models. In order to tangibly illustrate this process, this work develops a framework for a specific model problem combining: (I) a meshless discrete element “submodel” that tracks the trajectory of airborne hot particles/embers, subject to prevailing wind velocities and updrafts, (II) a topographical “submodel” of the ambient combustible material whereby airborne embers that make contact are allowed to start secondary fires (if conditions are appropriate), combined with ground-based surface spread and burn rates for generating new embers, new updrafts (due to hot air), etc., and (III) a Machine-Learning Algorithm to rapidly ascertain the multi-submodel system parameters that force the overall model to match observations. The submodels compute both ground and airborne hot-ember driven fire propagation, as well as subsequent distribution of debris/soot, which is important for air-quality assessment. The overall framework is designed for use in digital twin technology, which refers to an adaptive digital replica of a physical system, whereby model updates are continuously in near real-time. This necessitates a rapid simulation paradigm that can easily interface with telecommunications, cameras and sensors. The presented framework is designed to run quickly on laptops and hand held devices, with the guiding principle being to make it potentially useful for first-responders in real-time.

Fil: Waidelich, Sigfrido. Universidad Nacional de Río Negro. Laboratorio de Procesamiento de Señales Aplicado y Computación de Alto Rendimiento. Río Negro; Argentina. Fil: Denham, Mónica. Universidad Nacional de Río Negro. Laboratorio de Procesamiento de Señales Aplicado y Computación de Alto Rendimiento. Río Negro; Argentina. Fil: Denham, Mónica. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Laneri, Karina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fil: Laneri, Karina. Centro Atómico Bariloche.Grupo de Física Estadística e Interdisciplinaria. Río Negro; Argentina Our motivation comes from the need of a tailored computational tool for simulation and prediction of forest fire propagation, to be used by firefighters in Patagonia, Argentina. Based on previous works on Graphic Processing Units (GPU) for fitting and simulating fires in our region, we developed a visualization interface for real time computing, simulation and prediction of fire propagation. We have the possibility of changing the ensemble of raster maps layers to change the region in which fire will propagate. The visualization platform runs on GPUs and the user can rotate and zoom the landscape to select the optimal view of fire propagation. Opacity of different layers can be regulated by the user, allowing to see fire propagation at the same time that underlying vegetation, wind direction and intensity. The ignition point can also be selected by the user, and firebreaks can be plotted while simulation is going on. After the performance of a high number of stochastic simulations in parallel in GPUs, the application shows a map of the final fire surface colored according to the probability that a given cell burns. In this way the user can visually identify the most critical direction for fire propagation, a useful information to stop fire optimizing resources, which is specially important when they are scarce like is the case of our Patagonia region. Our motivation comes from the need of a tailored computational tool for simulation and prediction of forest fire propagation, to be used by firefighters in Patagonia, Argentina. Based on previous works on Graphic Processing Units (GPU) for fitting and simulating fires in our region, we developed a visualization interface for real time computing, simulation and prediction of fire propagation. We have the possibility of changing the ensemble of raster maps layers to change the region in which fire will propagate. The visualization platform runs on GPUs and the user can rotate and zoom the landscape to select the optimal view of fire propagation. Opacity of different layers can be regulated by the user, allowing to see fire propagation at the same time that underlying vegetation, wind direction and intensity. The ignition point can also be selected by the user, and firebreaks can be plotted while simulation is going on. After the performance of a high number of stochastic simulations in parallel in GPUs, the application shows a map of the final fire surface colored according to the probability that a given cell burns. In this way the user can visually identify the most critical direction for fire propagation, a useful information to stop fire optimizing resources, which is specially important when they are scarce like is the case of our Patagonia region.

Subway station fires often have serious consequences because of the high density of people and limited number of exits in a relatively enclosed space. In this study, a comprehensive model based on Bayesian network (BN) and the Delphi method is established for the rapid and dynamic assessment of the fire evolution process, and consequences, in underground subway stations. Based on the case studies of typical subway station fire accidents, 28 BN nodes are proposed to represent the evolution process of subway station fires, from causes to consequences. Based on expert knowledge and consistency processing by the Delphi method, the conditional probabilities of child BN nodes are determined. The BN model can quantitatively evaluate the factors influencing fire causes, fire proof/intervention measures, and fire consequences. The results show that the framework, combined with Bayesian network and the Delphi method, is a reliable tool for dynamic assessment of subway station fires. This study could offer insights to a more realistic analysis for emergency decision-making on fire disaster reduction, since the proposed approach could take into account the conditional dependency in the fire propagation process and incorporate fire proof/intervention measures, which is helpful for resilience and sustainability promotion of underground facilities.

International audience; We present an approach to plan trajectories for a fleet of fixed-wing UAVs to observe a wildfire evolving over time. Realistic models of the terrain, of the fire propagation process, and of the UAVs are exploited, together with a model of the wind. The approach tailors a generic Variable Neighborhood Search method to these models and associated constraints. Simulation results show ability to plan observation trajectories for a small fleet of UAVs, and to update the plans when new information on the fire are incorporated in the fire model.

Recent wildland urban interface fires have demonstrated the unrelenting destructive nature of these events and have called for an urgent need to address the problem. The Wildfire paradox reinforces the ideology that forest fires are inevitable and are actually beneficial; therefore focus should to be shifted towards minimizing potential losses to communities. This requires the development of vulnerability-based frameworks that can be used to provide holistic understanding of risk. In this study, we devise a probabilistic approach for quantifying community vulnerability to wildfires by applying concepts of graph theory. A directed graph for community in question is developed to model wildfire inside a community by incorporating different fire propagation modes. The model accounts for relevant community-specific characteristics including wind conditions, community layout, individual structural features, and the surrounding wildland vegetation. We calibrate the framework to study the infamous 1991 Oakland fire in an attempt to unravel the complexity of community fires. We use traditional centrality measures to identify critical behavior patterns and to evaluate the effect of fire mitigation strategies. Unlike current practice, the results are shown to be community-specific with substantial dependency of risk on meteorological conditions, environmental factors, and community characteristics and layout.

The paper presents a model to support evacuation plans design for fire emergency management in healthcare facilities. It relies on existing path analysis algorithms such as Dijkstra and fire propagation simulation, also evaluating the level of criticality typical of healthcare facilities such as patients’ speed based upon their illness and admission and architectural structure of wards. The algorithm automatically evaluates the safest evacuation path (which may not coincide with the shortest) for single typology of patient (ambulating, partially-ambulating, completely non-ambulating, auto-sufficient or not) and inpatient unit (ICU, ordinary ward, short-observation unit) in relation to the position of the fire trigger. The results of the algorithm are shown by using SVG-rendered graphic of existing hospital’s layout.

In the last decade, research was conducted to develop measurement solutions dedicated to forest fires and based on image processing and computer vision. Significant progress was achieved in developing such tools for fire propagation in controlled laboratory environments. However, these developments are not suitable for outdoor unstructured environments. Additionally, wildland fires cover large areas; this limits the use of vision-based ground systems. Unmanned Aerial Vehicles (UAV) with cameras for remote sensing are promising as their performance/price ratio is increasing over time. They can provide a low-cost alternative for the prevention, detection, propagation monitoring and real-time support for fire fighting. In this paper, we give an overview of past work dealing with the use of UAVs in the context of wildland and forest fires, and propose a framework based on cooperative UAVs and UGVs for fires monitoring on a larger scale.

Every year, forest and wildland fires affect more than 350 million hectares worldwide resulting in important environmental, economic, and social losses. To efficiently fight against this major risk, specific actions are deployed. The efficiency of these actions is tightly linked to the knowledge of the phenomena and in improving the tools for detecting, predicting, and understanding fire propagation. An important step for vision-based fire analysis, is the detection of fire pixels. In this work, we propose Deep-Fire a deep convolutional neural network for fire pixels detection and fire segmentation. The proposed technique is tested on a database of wildland fires. The obtained results, show that the proposed architecture gives a very high performance for the segmentation of wildland and forest fire areas in outdoor non-structured scenarios.

Wildfires are a major concern in Argentinian northwestern Patagonia and in many ecosystems and human societies around the world. We developed an efficient cellular automata model in Graphic Processing Units (GPUs) to simulate fire propagation. The graphical advantages of GPUs were exploited by overlapping wind direction, as well as vegetation, slope, and aspect maps, taking into account relevant landscape characteristics for fire propagation. Stochastic propagation was performed with a probability model that depends on aspect, slope, wind direction and vegetation type. Implementing a Genetic Algorithm search strategy we show, using simulated fires, that we recover the five parameter values that characterize fire propagation. The efficiency of the fire simulation procedure allowed us to also estimate the fire ignition point when it is unknown as well as its associated uncertainty, making this approach suitable for the analysis of fire spread based on maps of burnt areas without knowing the point of origin of the fires or how they spread. Fil: Denham, Mónica Malen. Universidad Nacional de Río Negro. Sede Andina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Laneri, Karina Fabiana. Comisión Nacional de Energía Atómica. Centro Atómico Bariloche; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina

Forest fire propagation prediction is a key point to fight against such hazards. Several models and simulators have been developed to predict forest fire propagation. These models require input parameters such as digital elevation map, vegetation map, and other parameters describing the vegetation and meteorological conditions. Coupling wind field model and forest fire propagation model improves accuracy prediction, but increases significantly prediction time. This fact is critical since propagation prediction must be provided in advance to allow the control centers to manage firefighters in the best possible way. This work analyses WindNinja execution time, describes a WindNinja parallelisation based on map partitioning, determines the limitations of such methodology for large maps and presents an improvement based on adapting map resolution to accomplish execution time limitations.

This paper presents a system for multi-agent simulation for passenger evacuation in case of the inclination of a ship under the storm. Evacuation process depends on many different factors such as environmental properties (e.g. wind velocity, intensity of waves), human behavioral aspects and so on. In order to describe passengers’ behavior in case of emergency evacuation considering the inclination of a ship, we applied a force-based model adapted from multi-agent system. Our approach provides an opportunity to estimate evacuation time depending on intensity of waves, rate of sailing and relative direction. Our proposed model can be coupled with the simulation of ship flooding or fire propagation. Having analyzed the results, these outputs could be applied to design contingency plans to assist the crew members in the frameworks of decision support systems (DSS).

This paper proposes a new multimodal stereovision framework for wildland fires detection and analysis. The proposed system uses near infrared and visible images to robustly segment the fires and extract their three-dimensional characteristics during propagation. It uses multiple multimodal stereovision systems to capture complementary views of the fire front. A new registration approach is proposed, it uses multisensory fusion based on GNSS and IMU data to extract the projection matrix that permits the representation of the 3D reconstructed fire in a common reference frame. The fire parameters are extracted in 3D space during fire propagation using the complete reconstructed fire. The obtained results show the efficiency of the proposed system for wildland fires research and firefighting decision support in operational scenarios.

International audience; The objective of this paper is to assess the reliability of a wireless sensor network (WSN) equipped with mini photovoltaic cells (PV-WSN), simulating forest fire conditions. Our assessment considers the hardware specifications of WSN sensors, photovoltaic (PV) cells, rechargeable batteries, communication protocols, and elements required for efficient fire detection. Our study’s main assumptions are that: the PV-WSN is structured randomly with one sink per cluster and identical sensors used, random losses due to environmental interference to the network and variability of PV-cell orientation are accounted for, the shape of forest fire propagation is uniform and elliptical, the surrounding vegetation is homogeneous, and packets lost during transmission are retransmitted with the use of a dynamic source routing protocol. Physical hardware specifications were included in our reliability assessments for each network type and a realistic battery re-charge/depletion scenario based on sensor states, sun availability, and PV efficiency was considered for a PV-WSN. We developed a simulator to study and to compare how the PV-WSN behaved differently under different forest fire conditions. Our results support the use of a PV-WSN, as network lifetime can be efficiently prolonged in the context of large-scale fire detection with this setup.

The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.

Forests fires are a significant problem especially in countries of the Mediterranean basin. To fight against these disasters, the accurate prediction of forest fire propagation is a crucial issue. Propagation models try to describe the future evolution of the forest fire given an initial scenario and certain input parameters. However, the data describing the real fire scenario are usually subject to high levels of uncertainty. Moreover, there are input parameters that present spatial and temporal variation that make the prediction less accurate. Therefore, to overcome such uncertainty and improve accuracy it is necessary to couple complementary models such as the case of the wind field model. Such models use the meteorological forecasted wind to provide the wind direction and speed depending on the topography of the terrain. We use WindNinja as wind field simulator. This simulator takes a lot of time to deliver the predictions and it is a serious problem because fire propagation prediction must accomplish strict time constraints. To solve this problem, we propose map partitioning and solving independently for each one of the parts. However, the model has problems concerning boundary effects which is an additional source of uncertainty. Therefore, it is necessary to apply certain degree of overlapping among parts to reach a stable wind field without inconsistencies and a minimum uncertainty.

The ability to estimate both the time and probability of a wildfire reaching an area to be protected is critically important to preventing loss of human life and property, and damage to ecological and economic assets. Wildfire decision trigger modelling has been used to assess fire exposure and create evacuation trigger buffers around the communities providing a specific amount of warning time. This approach has been applied in multiple scenarios including household-level and community-level evacuation planning and during suppression operations. However, little attention has been paid to input data uncertainty using this modelling approach. This study presents an innovative stochastic fire simulation decision trigger modelling method that produces a probability map of the fire arrival to areas to be protected by simulating (n) wildfire decision trigger buffers with varied input data according to a potential range of deviations. The Tubbs fire (USA) was used as case study to show the applicability of this approach to estimate the probability of wildland fire impact. Our results highlighted the importance of considering input data uncertainty in operational environments to estimate fire progression and decision trigger buffers to better develop suppression tactic and strategy. The method presented may be solved in real-time and used with any empirical fire propagation model as a core engine. Practical real-time implications of this fire simulation mode are discussed.

The Romanian norms and technical regulations do not provide the obligation of compartment fire tests for composite facade insulation systems “ETICS”. Unfortunately, only after these tests can result reliable data on the performance evaluation of ETICS under the effects of compartment fires, and there is still no harmonized European standard requiring such tests (in 2018 the first part of a European project that aims to harmonize the fire exposure test of façades was completed). In this context, researchers from the INCERC Fire Security Research and Testing Laboratory in Bucharest, tested experimentally the exposure to compartment fire of two types of ETICS, according to the test method set out in BS 8414. The climate test conditions of both systems were similar, and after the tests (for the first time in Romania) a series of data were obtained on the behaviour of ETICS under the action of compartment fires. With these results, the Romanian fire research aims to bring its contribution at European level, for the large-scale fire performance assessment of ETICS and the identification of innovative solutions for limiting fire propagation on building facades.

The accurate prediction of forest fire propagation is a crucial issue to minimize its effects. Several models have been developed to determine the forest fire propagation. Simulators implementing such models require diverse input parameters to deliver predictions about fire propagation. However, the data describing the actual scenario where the fire is taking place are usually subject to high levels of uncertainty. The input-data uncertainty represents a serious drawback for the correctness of the prediction. So, a two-stage methodol- ogy was developed to calibrate the input parameters in an adjustment stage so that the calibrated parameters are used in the prediction stage to improve the quality of the predictions. This way, we relieve the effects of such uncertainty. In this work, we take advantage of this two stage methodology applying Genetic Algorithms as the calibration technique. However, the use of Genetic Algorithms require the execution of many simulations. This fact, added to the eventual long executions of the underlying simulator (due to its inherent complexity), implies to deal with another serious problem: the time needed to deliver the predictions. To be useful, the prediction must be provided much faster than real time. So, it is necessary to exploit all available computing resources. In this work, we present a two-stage forest fire spread prediction hybrid MPI-OpenMP application based on the Master- Worker paradigm and the parallelization of the FARSITE simulator in order to minimize the response time. The results as regards the enhancement in the quality of the predictions are reported, as well as the results regarding the time saving obtained by this hybrid application.

Forest fire simulation is a challenging, complex problem which requires large amounts of data to be processed for an accurate simulation. This paper implements Rothermel's fire spread equations on three different spread methodologies. Both a sequential implementation of each spread methodology along with a parallel implementation are presented in this paper. The parallel version is implemented on the Graphics Processing Unit (GPU) using NVIDIA's CUDA programming language. The parallel spread methods achieved runtimes in those ranges realistic for fighting a real-time forest fire. The GPU implementation achieved faster running times in each of the spread methodologies, ranging from 64x to 229x faster than the sequential implementation.

A quick response becomes crucial in natural hazard management. When an emergency occurs, hazard evolution simulators are a very helpful tool for the teams in charge of making decisions. To perform the simulations, they rely on data which usually constitutes a big set of parameters, which have been previously recorded from observations, usually coming from remote sensors, images, etc. However, this data is frequently subject to a high degree of uncertainty. This data uncertainty also produces uncertainty in simulators’ results. To overcome this drawback, in previous works we developed a two-stage prediction method, which has been demonstrated to improve noticeably the quality of the predictions. The time incurred in performing this strategy, however, may vary significantly depending on different factors. As it is well known, the execution time of a particular simulator depends on the specific setting of the input parameters. Moreover, decision control centers in charge of making decisions to fight against the ongoing disaster require a certain degree of quality in the final prediction. Depending on how demanding are the quality and time constraints, the two-stage strategy may need the support of Urgent Computing solutions, in order to meet the requirements. In this work, we focus on forest fires spread prediction, as a real application case of study, and we expose our methodology to characterize both the time needed and the expected quality of the two-stage prediction method, so that we are able to determine the amount of computational resources needed to respond effciently to an eventual emergency. This way, we emphasize the need of Urgent Computing mechanisms to be able to put into practice this method at real time.

Natural hazards are significant problems that every year cause important loses around the world. A good prediction of the behavior of the hazards is a crucial issue to fight against them and to minimize the damages. The models that represent these phenomena need several input parameters and in many cases, such parameters are diffcult to know or even to estimate in a real scenario. So, a methodology based on the DDDAS paradigm was developed to calibrate the input parameters according to real observations of the behavior and evolution of the hazard. Such calibrated parameters are then used to provide an improved prediction for the next time interval. This methodology was tested on Forest Fire Propagation Prediction with significant results. The developed methodology takes the fire behavior and propagation during a time interval and then searches for the values of the input parameters that best reproduce the propagation of the fire during that interval. Several Artificial Intelligence (AI) methods were applied to carry out this search as fast as possible. The values of the parameters that best reproduce the behavior of the fire were then used as input parameters to predict the propagation during the next time interval. These parameters were considered constant during both time intervals and a single value for each parameter was used for the calibrating process and for the prediction stage. This methodology fits on the DDDAS paradigm since the prediction is dynamically driven by the system evolution. However, there are several parameters that are not constant through time, but they may vary dynamically. In the case of forest fires, a typical example is the wind. In some cases, when the time interval is short an average value for the wind can be a feasible value, but when the time interval is longer, in most cases, a single value cannot represent the variability of the wind. We can estimate wind behavior applying some complementary model. In this work, we are going a step further considering the dynamic behavior of such parameters. We propose an extension of the existing prediction scheme that takes into account the dynamically changing parameters by coupling a weather prediction system on a DDDAS Forest Fire Propagation Prediction system.

High-level control of fire is very attractive to artists, as it facilitates a detail-free user interface to make desirable flame effects. In this paper, a unified framework is proposed for modeling and animating fire under general geometric constraints and evolving rules. To capture the fire projection on user’s model animation, we develop a modified closest-point method (MCPM) to handle dynamic situations while maintaining the robustness of the closest-point method. A control blue core (CBC) is designed and generated automatically from the fire projection at each time step. It translates the geometric constraints and the user-specified evolving rules into implicit control conditions. Our L-Speed function leverages CBC’s shape information and conducts the large-scale motion of fire, leaving the basic physically-based model to refine simulation details. The experimental results show the effectiveness of our method for modeling fire propagation along complex curves or surfaces, or forming a flaming shape and following its motion.

Wind field is a critical issue in forest fire propagation prediction. However, wind field calculation is a complex problem that for large terrains involves solving huge linear systems. Solving such systems takes too much time and makes the approach unfeasible in real time operation. To overcome this problem the Schwarz alternating domain decomposition can be applied. Using this method the linear system is decomposed in a set of overlapped subdomains that can be solved in parallel using a Master/Worker paradigm and the wind field calculation time can be significantly reduced.

International Conference on Computational Science, ICCS 2015 – Computational Science at the Gates of Nature Forest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints. This research has been supported by the Ministerio de Economía y Competitividad (MECSpain) under contract TIN2011-28689-C02-01 and the Catalan government under grant 2014- SGR-576.

One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. We propose going one step beyond the classical wildland fire prediction by linking evolutionary optimization strategies to the traditional scheme with the aim of emulating an "ideal" fire propagation model as much as possible. In order to accelerate the fire prediction, this enhanced prediction scheme has been developed in a fashion on a Linux cluster using MPI. Furthermore, a sensitivity analysis has been carried out to determine the input parameters that we can fix to their typical values in order to reduce the search-space involved in the optimization process and, therefore, accelerates the whole prediction strategy.

We propose a hybrid contextual fire detection algorithm for airborne and satellite thermal images. The proposed algorithm essentially treats fire pixels as anomalies in images and can be considered a special case of the more general clutter or background suppression problem. It utilizes the local background around a potential fire pixel and discriminates fire pixels based on the squared Mahalanobis distance in multispectral feature space. It also employs the normalized thermal index to identify background fire pixels that should be excluded from the calculation of the statistical properties of the local background. The use of the squared Mahalanobis distance naturally incorporates the covariance of the multispectral image into the decision and requires the setting of a single detection threshold. By contrast, previous contextual algorithms only incorporate the statistical properties of individual bands and require the manual setting of multiple thresholds. Compared with the latest Moderate Resolution Imaging Spectroradiometer fire product (version 4), our algorithm improves user accuracy and producer accuracy by 1.5% and 2.6% on average, respectively, and up to 28% for some images. In addition, the novel use of the squared Mahalanobis distance allows us to create fire probability images that are useful for fire propagation modeling. As an example, we demonstrate this use for the airborne data.

Classical prediction fire schemes do not match the real fire propagation, basically, because of the complexity of the physical models involved, the need for a great amount of computation and the difficulties of providing accurate input parameters. We describe an enhanced prediction scheme, which uses recent fire history and optimization techniques to predict near future propagation. The proposed method takes advantage of the computational power offered by distributed systems to accelerate the optimization process at real time.

Entropy generation is commonly applied to describe the evolution of irreversible processes, such as heat transfer and turbulence. These are both dominating phenomena in fire propagation. In this paper, entropy generation analysis is applied to a grassland fire event, with the aim of finding possible links between entropy generation and propagation directions. The ultimate goal of such analysis consists in helping one to overcome possible limitations of the models usually applied to the prediction of wildfire propagation. These models are based on the application of the superimposition of the effects due to wind and slope, which has proven to fail in various cases. The analysis presented here shows that entropy generation allows a detailed analysis of the landscape propagation of a fire and can be thus applied to its quantitative description.

Wind speed and direction are parameters that affect forest fire propagation dramatically. So, an accurate estimation of such parameters is crucial to predict the fire propagation precisely. WindNInja is a wind field simulator that can easily be coupled to a forest fire propagation simulator such as FARSITE. However, wind field simulators present to main drawbacks: They take too much time to compute the wind field and they require a lot of memory. So, a map partitioning strategy has been developed to compute partial wind field maps that can be aggregated afterwards. Each map part can be computed in parallel and the amount of memory required is available in a single node. In this work a methodology to determine the most adequate map partitioning is presented. The map part shape, map part size, amount of overlapping and number of parts have been studied considering execution time and effects on wind field estimation. The results are based on a wide experimentation and are validated with real case scenarios.

Fire prevention is one of the main topics regarding home safety of old-renovated and newly built buildings. While it is possible to install modern fire extinguisher system in new houses, during house building, to prevent and stop fires in the old ones, it is easier and less expensive to place some portable automated system to perform this task. To create an automated system, a sensor to localize a fire in a closed environment such as a room is necessary. The sensor has to provide the coordinates of the fire onset to an automated arm with a fire extinguisher. The sensor is based upon a couple of matrix of thermopiles; as a result, their images are opportunely elaborated and they give the dimensional position of the fire onset where the fire extinguisher will direct its beam. This system is a very promising system in closed environment, due to its cost and relative simplicity of installation in order to better prevent fire propagation.

Forest fires lead to high amount of environmental and economic loss all over the world. Prevention and early detection efforts aim to eliminate or minimize the damage that will be caused by a fire incident. Current surveillance systems for forest fires do not provide dense real-time monitoring and hence they lack prevention or early detection of a fire threat. Wireless sensor networks (WSNs), on the other hand, can collect real-time information such as temperature and humidity from almost all points of a forest and can provide fresh and accurate data for the fire-fighting management center quickly. In this work, we aim to evaluate the reporting performance of a WSN under realistic workload. Since fires are destructive and burning a deployed WSN is not feasible, simulation is the appropriate way to assess the reporting capability of a WSN during a forest fire. We integrate WSN simulator with a realistic fire propagation simulator which is modified to provide time based temperature field information while the fire propagates through the deployment area. Temperature information is used for the generation of realistic workloads and the determination of sensor destruction times that affects the routing decisions in WSN simulations. We present the effects of WSN related factors; such as reporting rate, number of the sinks, and the sink locations together with the effects of environmental factors such as the wind speed and the number of ignition points in terms of temperature reporting performance and freshness of temperature map.

This paper contains a survey of recent work on a class of dynamic blocking problems. The basic model consists of a differential inclusion describing the growth of a set in the plane. To restrain its expansion, it is assumed that barriers can be constructed, in real time. Here the issues of major interest are: (i) whether the growth of the set can be eventually blocked, and (ii) what is the optimal location of the barriers, minimizing a cost criterion. After introducing the basic definitions and concepts, the paper reviews various results on the existence or non-existence of blocking strategies. A theorem on the existence of an optimal strategy is then recalled, together with various necessary conditions for optimality. Sufficient conditions for optimality and a numerical algorithm for the computation of optimal barriers are also discussed, together with several open problems.

This paper presents a new approach for the estimation of fire front volume in indoor laboratory experiments. This work deals with fire spreading on inclinable tables. The method is based on the use of two synchronized stereovision systems positioned respectively in a back position and in a front position of the fire propagation direction. The two vision systems are used in order to extract complementary 3D fire points. The obtained data are projected in a same reference frame and used to build a global form of the fire front. An inter-systems calibration procedure is presented and permits the computation of the projection matrix in order to project all the data to a unique reference frame. From the obtained 3D fire points, a three dimensional surface rendering is performed and the fire volume is estimated.

Every year forest fires provoke enormous looses from the ecological, economical and social point of view, by burning millions of hectares and killing several people. Nowadays, several forest fire simulators are used for helping in forest fire prevention and mitigation. Most of these simulators are based on Rothermel model. However, in most cases it is not possible to know the exact real-time values of model input parameters. This input data uncertainty causes predictions that are far from the real fire propagation. For this reason, we propose a fire propagation prediction system based on two stages: a calibration stage and a prediction stage. The calibration stage bases on the observed real fire evolution, so that the behavior of the fire is assimilated by the system at run-time matching the Dynamic Data Driven Application Systems (DDDAS) paradigm.