scholarly journals Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model

2020 ◽  
Vol 348 (8-9) ◽  
pp. 759-768
Author(s):  
M. H. Tchiekre ◽  
A. D. V. Brou ◽  
J. K. Adou
Keyword(s):  
Wildland Fire ◽  
Optimization Techniques ◽  
Propagation Model ◽  
Deterministic Optimization ◽  
Fire Propagation

Generation of synthetic infrared remote-sensing scenes of wildland fire

2009 ◽  
Vol 18 (3) ◽  
pp. 302 ◽  
Author(s):  
Zhen Wang ◽  
Anthony Vodacek ◽  
Janice Coen
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Ray Tracing ◽  
Wildland Fire ◽  
Propagation Model ◽  
Fire Propagation ◽  
Radiated Energy ◽  
Infrared Remote Sensing ◽  
And Behavior ◽  
Ray Tracing Model

We describe a method for generating synthetic infrared remote-sensing scenes of wildland fire. These synthetic scenes are an important step in data assimilation, which is defined as the process of incorporating new data into an executing model. In our case, this is a fire propagation model. The scenes are built using the surface output of fire position from a fire propagation code and prior knowledge of fire physics and behavior to estimate the shape of the flame. The scene radiance is then estimated by employing a physics-based ray-tracing model called DIRSIG to render the radiation that would reach a sensor on an airborne platform. Values of the Fire Radiated Energy calculated from the synthetic radiance scene compare well with previously published values, providing validation of the method.

scholarly journals Modeling wildland fire propagation with level set methods

2009 ◽  
Vol 57 (7) ◽  
pp. 1089-1101 ◽  
Author(s):  
V. Mallet ◽  
D.E. Keyes ◽  
F.E. Fendell
Keyword(s):  
Level Set ◽  
Wildland Fire ◽  
Level Set Methods ◽  
Fire Propagation

scholarly journals Parameter Estimation for the Forest Fire Propagation Model

2020 ◽  
Vol 75 (1) ◽  
pp. 1-22
Author(s):  
Martin Ambroz ◽  
Karol Mikula ◽  
Marek Fraštia ◽  
Marián Marčiš
Keyword(s):  
Data Assimilation ◽  
Forest Fire ◽  
Hausdorff Distance ◽  
Small Time ◽  
Propagation Model ◽  
Model Parameters ◽  
Normal Velocity ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Fire Front

AbstractThis paper first gives a brief overview of the Lagrangian forest fire propagation model [Ambroz, M.—Balažovjech, M.—Medl’a, M.—Mikula, K.: Numerical modeling of wildland surface fire propagation by evolving surface curves, Adv. Comput. Math. 45 (2019), no. 2, 1067–1103], which we apply to grass-field areas. Then, we aim to estimate the optimal model parameters. To achieve this goal, we use data assimilation of the measured data. From the data, we are able to estimate the normal velocity of the fire front (rate of spread), dominant wind direction and selected model parameters. In the data assimilation process, we use the Hausdorff distance as well as the Mean Hausdorff distance as a criterion. Moreover, we predict the fire propagation in small time intervals.

scholarly journals Analysis of the effects of biases in ESP forecasts on electricity production in hydropower reservoir management

2018 ◽  
Author(s):  
Richard Arsenault ◽  
Pascal Côté
Keyword(s):  
Hydrological Modeling ◽  
Reservoir Management ◽  
Optimization Techniques ◽  
Electricity Production ◽  
Weather Data ◽  
Climate Data ◽  
Streamflow Prediction ◽  
Test Bed ◽  
Time Step ◽  
Deterministic Optimization

Abstract. This paper presents an analysis of the effects of biased Extended Streamflow Prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon testbed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data was fed to the hydrological model and the synthetic streamflow thus generated was considered as a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method’s response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test-bed in which the entire system is precisely and accurately modeled. The ensemble climate data forecasts, the hydrological modeling and ESP forecast generation, optimization model and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focused on one hydropower system both with and without minimum base load constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore increases the odds of forced spillage by attempting to maximize short-term profit by keeping a higher net head. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider optimal trajectories.

Flatland in flames: a two-dimensional crown fire propagation model

2009 ◽  
Vol 18 (5) ◽  
pp. 527 ◽  
Author(s):  
James D. Dickinson ◽  
Andrew P. Robinson ◽  
Paul E. Gessler ◽  
Richy J. Harrod ◽  
Alistair M. S. Smith
Keyword(s):  
Bulk Density ◽  
Original Data ◽  
Propagation Model ◽  
Crown Fire ◽  
Wagner Model ◽  
Fire Propagation ◽  
Rate Of Spread ◽  
Proposed Model ◽  
Foliar Biomass ◽  
Canopy Bulk Density

The canopy bulk density metric is used to describe the fuel available for combustion in crown fire models. We propose modifying the Van Wagner crown fire propagation model, used to estimate the critical rate of spread necessary to sustain active crown fire, to use foliar biomass per square metre instead of canopy bulk density as the fuel input. We tested the efficacy of our proposed model by comparing predictions of crown fire propagation with Van Wagner’s original data. Our proposed model correctly predicted each instance of crown fire presented in the seminal study. We then tested the proposed model for statistical equivalence to the original Van Wagner model using two contemporary techniques to parameterize canopy bulk density. We found the proposed and original models to be statistically equivalent when canopy bulk density was parameterized using the method incorporated in the Fire and Fuels Extension to the Forest Vegetation Simulator (difference < 0.5 km h–1, α = 0.05, n = 2626), but not when parameterized using the method of Cruz and others. Use of foliar biomass per unit area in the proposed model makes for more accurate and easily obtained fuel estimates without sacrificing the utility of the Van Wagner model.

scholarly journals Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

2019 ◽  
Vol 23 (6) ◽  
pp. 2735-2750 ◽  
Author(s):  
Richard Arsenault ◽  
Pascal Côté
Keyword(s):  
Reservoir Management ◽  
Optimization Techniques ◽  
Electricity Production ◽  
Weather Data ◽  
Climate Data ◽  
Streamflow Prediction ◽  
Test Bed ◽  
Time Step ◽  
Deterministic Optimization ◽  
Management Optimization

Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

scholarly journals Modeling wildland fire propagation using a semi-physical network model

2015 ◽  
Vol 4 ◽  
pp. 11-18 ◽  
Author(s):  
J.K. Adou ◽  
A.D.V. Brou ◽  
B. Porterie
Keyword(s):  
Network Model ◽  
Wildland Fire ◽  
Fire Propagation ◽  
Physical Network

scholarly journals Evaluation of wildland fire smoke plume dynamics and aerosol load using UV scanning lidar and fire–atmosphere modelling during the Mediterranean Letia 2010 experiment

2014 ◽  
Vol 14 (3) ◽  
pp. 509-523 ◽  
Author(s):  
V. Leroy-Cancellieri ◽  
P. Augustin ◽  
J. B. Filippi ◽  
C. Mari ◽  
M. Fourmentin ◽  
...  
Keyword(s):  
Wildland Fire ◽  
Mediterranean Area ◽  
Atmospheric Model ◽  
Vertical Position ◽  
Passive Tracer ◽  
Smoke Plume ◽  
Fire Propagation ◽  
Lidar Measurements ◽  
Fire Smoke ◽  
Scanning Lidar

Abstract. Vegetation fires emit large amount of gases and aerosols which are detrimental to human health. Smoke exposure near and downwind of fires depends on the fire propagation, the atmospheric circulations and the burnt vegetation. A better knowledge of the interaction between wildfire and atmosphere is a primary requirement to investigate fire smoke and particle transport. The purpose of this paper is to highlight the usefulness of an UV scanning lidar to characterise the fire smoke plume and consequently validate fire–atmosphere model simulations. An instrumented burn was conducted in a Mediterranean area typical of ones frequently subject to wildfire with low dense shrubs. Using lidar measurements positioned near the experimental site, fire smoke plume was thoroughly characterised by its optical properties, edge and dynamics. These parameters were obtained by combining methods based on lidar inversion technique, wavelet edge detection and a backscatter barycentre technique. The smoke plume displacement was determined using a digital video camera coupled with the lidar. The simulation was performed using a mesoscale atmospheric model in a large eddy simulation configuration (Meso-NH) coupled to a fire propagation physical model (ForeFire), taking into account the effect of wind, slope and fuel properties. A passive numerical scalar tracer was injected in the model at fire location to mimic the smoke plume. The simulated fire smoke plume width remained within the edge smoke plume obtained from lidar measurements. The maximum smoke injection derived from lidar backscatter coefficients and the simulated passive tracer was around 200 m. The vertical position of the simulated plume barycentre was systematically below the barycentre derived from the lidar backscatter coefficients due to the oversimplified properties of the passive tracer compared to real aerosol particles. Simulated speed and horizontal location of the plume compared well with the observations derived from the videography and lidar method, suggesting that fire convection and advection were correctly taken into account.

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