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

TL;DR

This paper introduces a spatio-temporal neural network framework for emulating wildfire spread models, achieving high accuracy and robustness with limited training data, thus offering a more efficient alternative to traditional simulations.

Contribution

It presents a novel neural network approach capable of emulating complex fire spread models at high resolutions, even with small training datasets, using innovative data augmentation techniques.

Findings

Average Jaccard score of 0.76 indicating good emulation accuracy

Effective at high spatial and temporal resolutions

Robust performance with limited training data

Abstract

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.