Parameter estimation of fire propagation models using level set methods

被引:28
作者
Alessandri, Angelo [1 ]
Bagnerini, Patrizia [1 ]
Gaggero, Mauro [2 ]
Mantelli, Luca [1 ]
机构
[1] Univ Genoa, Dept Mech Engn, I-16145 Genoa, Italy
[2] Natl Res Council Italy, Inst Marine Engn, I-16149 Genoa, Italy
关键词
Wildland fire propagation model; Level set methods; Parameter estimation; Optimization; WILDLAND FIRE; WILDFIRE SPREAD; CELLULAR-AUTOMATA; SIMULATION; PREDICTION;
D O I
10.1016/j.apm.2020.11.030
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The availability of wildland fire propagation models with parameters estimated in an accurate way starting from measurements of fire fronts is crucial to predict the evolution of fire and allocate resources for firefighting. Thus, we propose an approach to estimate the parameters of a wildland fire propagation model combining an empirical rate of spread and level set methods to describe the evolution of the fire front over time and space. The estimation of parameters affecting the rate of spread is performed by using fire front shapes measured at different time instants as well as wind velocity and direction, landscape elevation, and vegetation distribution. Parameter estimation is done by solving an optimization problem, where the objective function to be minimized is the symmetric difference between predicted and measured fronts at different time instants. Numerical results obtained by the application of the proposed method are reported in two simulated scenarios and in a case study based on data originated by the 2002 Troy fire in Southern California. The obtained results showcase the effectiveness of the proposed approach both from qualitative and quantitative viewpoints. (c) 2020 Elsevier Inc. All rights reserved.
引用
收藏
页码:731 / 747
页数:17
相关论文
共 48 条
[1]  
Alessandri A, 2020, AUTOMATICA, V117, P1
[2]   Optimal Propagating Fronts Using Hamilton-Jacobi Equations [J].
Alessandri, Angelo ;
Bagnerini, Patrizia ;
Cianci, Roberto ;
Gaggero, Mauro .
MATHEMATICS, 2019, 7 (11)
[3]   Optimal Control of Propagating Fronts by Using Level Set Methods and Neural Approximations [J].
Alessandri, Angelo ;
Bagnerini, Patrizia ;
Gaggero, Mauro .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2019, 30 (03) :902-912
[4]   A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990 [J].
Alexandridis, A. ;
Vakalis, D. ;
Siettos, C. I. ;
Bafas, G. V. .
APPLIED MATHEMATICS AND COMPUTATION, 2008, 204 (01) :191-201
[5]  
Ambroz M., 2018, Tatra Mountains Mathematical Publications, V72, P1
[6]   Fire models and methods to map fuel types: The role of remote sensing [J].
Arroyo, Lara A. ;
Pascual, Cristina ;
Manzanera, Jose A. .
FOREST ECOLOGY AND MANAGEMENT, 2008, 256 (06) :1239-1252
[7]   Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level [J].
Artes, Tomas ;
Cencerrado, Andres ;
Cortes, Ana ;
Margalef, Tomas ;
Rodriguez-Aseretto, Dario ;
Petroliagkis, Thomas ;
San-Miguel-Ayanz, Jesus .
2014 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, 2014, 29 :1216-1226
[8]   OPTIMAL CONTROL OF THE CLASSICAL TWO-PHASE STEFAN PROBLEM IN LEVEL SET FORMULATION [J].
Bernauer, Martin K. ;
Herzog, Roland .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2011, 33 (01) :342-363
[9]  
Bertsekas D.P., 2016, NONLINEAR PROGRAMMIN
[10]   A comparison of level set and marker methods for the simulation of wildland fire front propagation [J].
Bova, Anthony S. ;
Mell, William E. ;
Hoffman, Chad M. .
INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2016, 25 (02) :229-241