Mathematical models and calculation systems for the study of wildland fire behaviour

By E. Pastor, Luis Gonzaga Alonso Zárate, E. Planas, J. Arnaldos. 2003

350 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/69c8943cbf4c1d8dc9b187e8d4f0b83319ad52d9

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Citations in the corpus

A Review on Forest Fire Detection Techniques (2014, background): “The researchers definedmore than 27mathematicalmodels to describe the fire behaviour where they stated that those models developed according to different countries experience of forest fire and each model is different according to the input parameters and the environments nature (fuel indexing [22, ”
A mathematical model for predicting the maximum potential spotting distance from a crown fire (2012, background): “…work on the general topic of spotting in wildland fires has appeared in print sporadically for many years (e.g. Tarifa et al. 1965; Cheney and Bary 1969; Lee and Hellman 1970; Grishin et al. 1981; Ellis 2011), as have periodic reviews (e.g. Babrauskas 2003; Pastor et al. 2003; Koo et al. 2010).”
Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models (2007, background): “Recent reviews (e.g. Perry (1998); Pastor et al. (2003); etc), while endeavouring to be comprehensive, have offered only superficial and cursory inspections of the models presented.”
Crown fire behaviour in a northern jack pine-black spruce forest (2004, positive): “This realization, coupled with the need to improve upon the predictive capability of the (Rothermel 1991) crown fire rate of spread model (Scott and Reinhardt 2001; Pastor et al. 2003), were principal factors in the genesis of the International Crown Fire Modelling Experiment (ICFME).”
A review of wildland fire spread modelling, 1990-present, 1: Physical and quasi-physical models (2007, background): “Recent reviews (e.g. Perry (1998); Pastor et al. (2003); etc), while endeavouring to be comprehensive, have offered only superficial and cursory inspections of the models presented.”
A physics-based approach to modelling grassland fires (2007, background): “Review of physics-based wildland fire models Fire models can be classified into three types (Pastor et al. 2003): empirical, semi-empirical, or theoretical.”
A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area (2017, background): “All rights reserved. potential fire behaviors through a set mathematical equations of fluid mechanics, combustions of canopy biomass, and heat transfer mechanisms (Pastor et al., 2003).”