Predicting fire spread and behaviour on the fireline. Wildfire analyst pocket: A mobile app for wildland fire prediction

被引:35
|
作者
Monedero, Santiago [1 ]
Ramirez, Joaquin [2 ]
Cardil, Adrian [1 ]
机构
[1] Tecnosylva, Parque Tecnol Leon, Leon 24009, Spain
[2] UCSD Calit2 Qualcomm Inst, Technosylva, La Jolla, CA 92037 USA
关键词
Fire modeling; Fire behaviour; Fire management; Surface fire; Prescribed burning; MODEL; TIME;
D O I
10.1016/j.ecolmodel.2018.11.016
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Accurately predicting fire spread and behaviour on the fireline, in the field, is highly important in order to prevent the loss of human life, improve the success of initial attack and better understand the potential fire behaviour, minimizing many risks for firefighters. We present the Wildfire Analyst (TM) Pocket Edition application (WFA Pocket), a mobile tool aimed to be used by the fire fighter community. It shows punctual fire characteristics and estimated progression based on user introduced input data in an intuitive 3D map interface in real-time, allowing the user to interactively change parameters and analyze how the fire behaviour changes in relation to the inputs. The mathematical models implemented are all well-known by the scientific community and reported in this article. The application has integrated GIS capabilities, can work online and offline, and can retrieve fuel, weather and canopy data from online servers for the georeferenced ignition point. We describe the background and model foundation of WFA Pocket as well as its system design and main features. We also evaluate its robustness of results and present a case study to show the potential use of this tool in the field. Limitations and assumptions in the use of the application as well as potential improvements for the future are discussed.
引用
收藏
页码:103 / 107
页数:5
相关论文
共 10 条
  • [1] Streamlined wildland-urban interface fire tracing (SWUIFT): Modeling wildfire spread in communities
    Masoudvaziri, Nima
    Bardales, Fernando Szasdi
    Keskin, Oguz Kaan
    Sarreshtehdari, Amir
    Sun, Kang
    Elhami-Khorasani, Negar
    ENVIRONMENTAL MODELLING & SOFTWARE, 2021, 143
  • [2] Improving wildland fire spread prediction using deep U-Nets
    Khennou, Fadoua
    Akhloufi, Moulay A.
    SCIENCE OF REMOTE SENSING, 2023, 8
  • [3] The distributed strategy for asynchronous observations in data-driven wildland fire spread prediction
    Zha, Mengxia
    Wang, Zheng
    Ji, Jie
    Zhu, Jiping
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2024, 33 (07)
  • [4] Evaluating a model for predicting active crown fire rate of spread using wildfire observations
    Alexander, Martin E.
    Cruz, Miguel G.
    CANADIAN JOURNAL OF FOREST RESEARCH, 2006, 36 (11) : 3015 - 3028
  • [5] Pyros: a raster-vector spatial simulation model for predicting wildland surface fire spread and growth
    Voltolina, Debora
    Cappellini, Giacomo
    Apuani, Tiziana
    Sterlacchini, Simone
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2024, 33 (03)
  • [6] Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms
    Li, Xingdong
    Wang, Xinyu
    Sun, Shufa
    Wang, Yangwei
    Li, Sanping
    Li, Dandan
    FIRE TECHNOLOGY, 2023, 59 (05) : 2683 - 2717
  • [7] Predicting the Wildland Fire Spread Using a Mixed-Input CNN Model with Both Channel and Spatial Attention Mechanisms
    Xingdong Li
    Xinyu Wang
    Shufa Sun
    Yangwei Wang
    Sanping Li
    Dandan Li
    Fire Technology, 2023, 59 : 2683 - 2717
  • [8] A cellular automata model for forest fire spread prediction: The case of the wildfire that swept through Spetses Island in 1990
    Alexandridis, A.
    Vakalis, D.
    Siettos, C. I.
    Bafas, G. V.
    APPLIED MATHEMATICS AND COMPUTATION, 2008, 204 (01) : 191 - 201
  • [9] Altered vegetation structure from mechanical thinning treatments changed wildfire behaviour in the wildland-urban interface on the 2011 Wallow Fire, Arizona, USA
    Johnson, Morris C.
    Kennedy, Maureen C.
    INTERNATIONAL JOURNAL OF WILDLAND FIRE, 2019, 28 (03) : 216 - 229
  • [10] A Deep Learning Framework: Predicting Fire Radiative Power From the Combination of Polar-Orbiting and Geostationary Satellite Data During Wildfire Spread
    Dong, Zixun
    Zheng, Change
    Zhao, Fengjun
    Wang, Guangyu
    Tian, Ye
    Li, Hongchen
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 10827 - 10841