Predicting the flame characteristics and rate of spread in fires propagating in a bed of Pinus pinaster using Artificial Neural Networks

Khaled Chetehouna (1) , Eddy El Tabach (2) , Loubna Bouazaoui (3) , Nicolas Gascoin (3, 2, 1)

1 INSA CVL - Institut National des Sciences Appliquées - Centre Val de Loire
2 PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique
3 LEES - Laboratoire Énergétique Explosions Structures [1998-2007]

Physical and geometrical characteristics of flame propagation are very important to better understand the forest fire spread behaviour and to improve risk management tools. Having a tool to predict these characteristics is of practical and theoretical interest for a better understanding of the complex chemical and physical mechanisms which occur during forest fire phenomena. A metamodel is presented based on Artificial Neural Networks (ANNs) for estimating physical and geometrical parameters of the forest fire front, namely the rate of spread (ROS), flame height (H f) and flame tilt angle (α f). The ANN was developed using literature data obtained from experiments of fire propagation in beds of Pinus pinaster needles. The optimal feedforward ANN architecture with error backpropagation (BPNN) was determined by the cross validation method. The ANN architecture having 5 hidden neurons

Domaines: Chimie analytique; Génie des procédés; Milieux fluides et réactifs; Mécanique des fluides [physics.class-ph]

Khaled Chetehouna, Eddy El Tabach, Loubna Bouazaoui, Nicolas Gascoin. Predicting the flame characteristics and rate of spread in fires propagating in a bed of Pinus pinaster using Artificial Neural Networks. Process Safety and Environmental Protection, 2015, 98, pp.50-56. ⟨10.1016/j.psep.2015.06.010⟩. ⟨hal-01253272⟩