scholarly journals Emulation of wildland fire spread simulation using deep learning

2021 ◽  
Vol 141 ◽  
pp. 184-198
Author(s):  
Frédéric Allaire ◽  
Vivien Mallet ◽  
Jean-Baptiste Filippi
Keyword(s):  
Deep Learning ◽  
Wildland Fire ◽  
Fire Spread

scholarly journals A Deep Learning Approach to Downscale Geostationary Satellite Imagery for Decision Support in High Impact Wildfires

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 294
Author(s):  
Nicholas F. McCarthy ◽  
Ali Tohidi ◽  
Yawar Aziz ◽  
Matt Dennie ◽  
Mario Miguel Valero ◽  
...  
Keyword(s):  
Deep Learning ◽  
Decision Support ◽  
Real Time ◽  
Forest Fires ◽  
Wildland Fire ◽  
Fire Spread ◽  
Low Earth Orbit ◽  
Practical Implementation ◽  
Spatiotemporal Resolution ◽  
Active Fire

Scarcity in wildland fire progression data as well as considerable uncertainties in forecasts demand improved methods to monitor fire spread in real time. However, there exists at present no scalable solution to acquire consistent information about active forest fires that is both spatially and temporally explicit. To overcome this limitation, we propose a statistical downscaling scheme based on deep learning that leverages multi-source Remote Sensing (RS) data. Our system relies on a U-Net Convolutional Neural Network (CNN) to downscale Geostationary (GEO) satellite multispectral imagery and continuously monitor active fire progression with a spatial resolution similar to Low Earth Orbit (LEO) sensors. In order to achieve this, the model trains on LEO RS products, land use information, vegetation properties, and terrain data. The practical implementation has been optimized to use cloud compute clusters, software containers and multi-step parallel pipelines in order to facilitate real time operational deployment. The performance of the model was validated in five wildfires selected from among the most destructive that occurred in California in 2017 and 2018. These results demonstrate the effectiveness of the proposed methodology in monitoring fire progression with high spatiotemporal resolution, which can be instrumental for decision support during the first hours of wildfires that may quickly become large and dangerous. Additionally, the proposed methodology can be leveraged to collect detailed quantitative data about real-scale wildfire behaviour, thus supporting the development and validation of fire spread models.

Simulation and thermal imaging of the 2006 Esperanza Wildfire in southern California: application of a coupled weather–wildland fire model

2014 ◽  
Vol 23 (6) ◽  
pp. 755 ◽  
Author(s):  
Janice L. Coen ◽  
Philip J. Riggan
Keyword(s):  
Thermal Imaging ◽  
Large Scale ◽  
Transient Flow ◽  
Wildland Fire ◽  
Fire Spread ◽  
Early Morning ◽  
Fuel Load ◽  
Dynamic Interactions ◽  
Riverside County ◽  
Layer Flow

The 2006 Esperanza Fire in Riverside County, California, was simulated with the Coupled Atmosphere–Wildland Fire Environment (CAWFE) model to examine how dynamic interactions of the atmosphere with large-scale fire spread and energy release may affect observed patterns of fire behaviour as mapped using the FireMapper thermal-imaging radiometer. CAWFE simulated the meteorological flow in and near the fire, the fire’s growth as influenced by gusty Santa Ana winds and interactions between the fire and weather through fire-induced winds during the first day of burning. The airflow was characterised by thermally stratified, two-layer flow channelled between the San Bernardino and San Jacinto mountain ranges with transient flow accelerations driving the fire in Cabazon Peak’s lee. The simulation reproduced distinguishing features of the fire including its overall direction and width, rapid spread west-south-westward across canyons, spread up canyons crossing its southern flank, splitting into two heading regions and feathering of the fire line. The simulation correctly depicted the fire’s location at the time of an early-morning incident involving firefighter fatalities. It also depicted periods of deep plume growth, but anomalously described downhill spread of the head of the fire under weak winds that was less rapid than observed. Although capturing the meteorological flow was essential to reproducing the fire’s evolution, fuel factors including fuel load appeared to play a secondary role.

Mantras of wildland fire behaviour modelling: facts or fallacies?

2017 ◽  
Vol 26 (11) ◽  
pp. 973 ◽  
Author(s):  
Miguel G. Cruz ◽  
Martin E. Alexander ◽  
Andrew L. Sullivan
Keyword(s):  
Wildland Fire ◽  
Original Data ◽  
Fire Spread ◽  
Empirical Models ◽  
Physical Models ◽  
Fire Behaviour ◽  
Fire Science ◽  
Current State ◽  
Valid Data ◽  
Insight Into

Generalised statements about the state of fire science are often used to provide a simplified context for new work. This paper explores the validity of five frequently repeated statements regarding empirical and physical models for predicting wildland fire behaviour. For empirical models, these include statements that they: (1) work well over the range of their original data; and (2) are not appropriate for and should not be applied to conditions outside the range of the original data. For physical models, common statements include that they: (3) provide insight into the mechanisms that drive wildland fire spread and other aspects of fire behaviour; (4) give a better understanding of how fuel treatments modify fire behaviour; and (5) can be used to derive simplified models to predict fire behaviour operationally. The first statement was judged to be true only under certain conditions, whereas the second was shown not to be necessarily correct if valid data and appropriate modelling forms are used. Statements three through five, although theoretically valid, were considered not to be true given the current state of knowledge regarding fundamental wildland fire processes.

Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China

2019 ◽  
Vol 28 (3) ◽  
pp. 205 ◽  
Author(s):  
Longyan Cai ◽  
Hong S. He ◽  
Yu Liang ◽  
Zhiwei Wu ◽  
Chao Huang
Keyword(s):  
Monte Carlo ◽  
Wildland Fire ◽  
Time Lag ◽  
Fire Spread ◽  
Model Parameters ◽  
Spread Rate ◽  
North East ◽  
Fire Modelling ◽  
Fuel Model ◽  
Model Parameter Uncertainty

Fire propagation is inevitably affected by fuel-model parameters during wildfire simulations and the uncertainty of the fuel-model parameters makes forecasting accurate fire behaviour very difficult. In this study, three different methods (Morris screening, first-order analysis and the Monte Carlo method) were used to analyse the uncertainty of fuel-model parameters with FARSITE model. The results of the uncertainty analysis showed that only a few fuel-model parameters markedly influenced the uncertainty of the model outputs, and many of the fuel-model parameters had little or no effect. The fire-spread rate is the driving force behind the uncertainty of other fire behaviours. Thus, the highly uncertain fuel-model parameters associated with spread rate should be used cautiously in wildfire simulations. Monte Carlo results indicated that the relationship between model input and output was non-linear and neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. Additionally, fuel-model parameters have high input uncertainty. Therefore, fuel-model parameters must be calibrated against actual fires. The highly uncertain fuel-model parameters with high spatial-temporal variability consisted of fuel-bed depth, live-shrub loading and 1-h time-lag loading are preferentially chosen as parameters to calibrate several wildfires.

scholarly journals Coupled atmosphere-wildland fire modeling with WRF-Fire version 3.3

2011 ◽  
Vol 4 (1) ◽  
pp. 497-545 ◽  
Author(s):  
J. Mandel ◽  
J. D. Beezley ◽  
A. K. Kochanski
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Wildland Fire ◽  
Surface Wind ◽  
Numerical Algorithms ◽  
Fire Spread ◽  
Weather Research And Forecasting ◽  
Time Step ◽  
Spread Model ◽  
Fire Spread Model

Abstract. We describe the physical model, numerical algorithms, and software structure of WRF-Fire. WRF-Fire consists of a fire-spread model, implemented by the level-set method, coupled with the Weather Research and Forecasting model. In every time step, the fire model inputs the surface wind, which drives the fire, and outputs the heat flux from the fire into the atmosphere, which in turn influences the atmosphere. The level-set method allows submesh representation of the burning region and flexible implementation of various kinds of ignition. WRF-Fire is distributed as a part of WRF and it uses the WRF parallel infrastructure for parallel computing.

Physical model of wildland fire spread: Parametric uncertainty analysis

2020 ◽  
Vol 217 ◽  
pp. 285-293
Author(s):  
Xieshang Yuan ◽  
Naian Liu ◽  
Xiaodong Xie ◽  
Domingos X. Viegas
Keyword(s):  
Uncertainty Analysis ◽  
Physical Model ◽  
Wildland Fire ◽  
Parametric Uncertainty ◽  
Fire Spread

A cellular automata model to link surface fires to firebrand lift-off and dispersal

2013 ◽  
Vol 22 (4) ◽  
pp. 428 ◽  
Author(s):  
Holly A. Perryman ◽  
Christopher J. Dugaw ◽  
J. Morgan Varner ◽  
Diane L. Johnson
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Loading ◽  
Cellular Automata Model ◽  
Rate Of Spread ◽  
Lift Off ◽  
Fuel Moisture Content ◽  
Spot Fires

In spite of considerable effort to predict wildland fire behaviour, the effects of firebrand lift-off, the ignition of resulting spot fires and their effects on fire spread, remain poorly understood. We developed a cellular automata model integrating key mathematical models governing current fire spread models with a recently developed model that estimates firebrand landing patterns. Using our model we simulated a wildfire in an idealised Pinus ponderosa ecosystem. Varying values of wind speed, surface fuel loading, surface fuel moisture content and canopy base height, we investigated two scenarios: (i) the probability of a spot fire igniting beyond fuelbreaks of various widths and (ii) how spot fires directly affect the overall surface fire’s rate of spread. Results were averages across 2500 stochastic simulations. In both scenarios, canopy base height and surface fuel loading had a greater influence than wind speed and surface fuel moisture content. The expected rate of spread with spot fires occurring approached a constant value over time, which ranged between 6 and 931% higher than the predicted surface fire rate of spread. Incorporation of the role of spot fires in wildland fire spread should be an important thrust of future decision-support technologies.

Numerical Prediction of the Fire Spread in Vegetative Fuels Using NISTWFDS Model

2015 ◽  
Vol 20 ◽  
pp. 177-192 ◽  
Author(s):  
Hadj Miloua
Keyword(s):  
Forest Fires ◽  
Wildland Fire ◽  
Radiation Heat Transfer ◽  
Fire Behavior ◽  
Reaction Diffusion ◽  
Fire Spread ◽  
Three Dimensions ◽  
Forest Stands ◽  
Fire Dynamics ◽  
Wind Climate

Current study focuses to the application of an advanced physics-based (reaction–diffusion) fire behavior model to the fires spreading through surface vegetation such as grasslands and elevated vegetation such as trees present in forest stands. This model in three dimensions, called Wildland Fire Dynamics Simulator WFDS, is an extension, to vegetative fuels, of the structural FDS developed at NIST. For simplicity, the vegetation was assumed to be uniformly distributed in a tree crown represented by a well defined geometric shape. This work on will focus on predictions of thermal function such as the radiation heat transfer and and thermal function for diverse cases of spatial distribution of vegetation in forest stands. The influence of wind, climate characteristics and terrain topography will also be used to extend and validate the model. The results obtained provide a basis to carry out a risk analysis for fire spread in the studied vegetative fuels in the Mediterranean forest fires.

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