Generalized Byram's formula for arbitrary fire front geometries

Zekri, N.; Harrouz, O.; Kaiss, A.; Clerc, Jean-Pierre; Viegas, Domingos X.

doi:10.1016/j.ijthermalsci.2016.07.006

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In some cases an estimate may incorrectly place the fire slightly ahead of an obstacle such as a body of water. If we consider this as the source of a new firefront propagating through open terrain, then the burned area by the new front will increase quadratically in time [28]. By training the model over a single time interval we limit the training penalty of these mismatches.…”

Section: Design Featuresmentioning

confidence: 99%

A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models

Bolt

Huston

Kuhnert

et al. 2022

2022 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)

View full text Add to dashboard Cite

Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbations in conditions can often lead to significant changes in fire spread (such as speed and direction), necessitating a computationally expensive large set of simulations to quantify uncertainty. Model emulation seeks alternative representations of physical models using machine learning, aiming to provide more efficient and/or simplified surrogate models. We propose a dedicated spatio-temporal neural network based framework for model emulation, able to capture the complex behaviour of fire spread models. The proposed approach can approximate forecasts at fine spatial and temporal resolutions that are often challenging for neural network based approaches. Furthermore, the proposed approach is robust even with small training sets, due to novel data augmentation methods. Empirical experiments show good agreement between simulated and emulated firefronts, with an average Jaccard score of 0.76.

show abstract

Section: Design Featuresmentioning

confidence: 99%