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

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.

A Novel Approach for Predicting Large Wildfires Using Machine Learning towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States

“…The model shows a promising result in basic conditions as the prediction closely matches the actual fire boundary. However, it is computationally demanding, requiring integration of many variables, and the model's accuracy varies widely across wildfires in different regions [16]. Another model, FIRECAST, is a convolutional neural network (CNN) used to predict the expected burned area of an active fire after 24 h [17].…”

Building trust in hazard model uncertainties

“…We discuss a fire emulator (using a neural net architecture) that is hybrid in approach Bolt et al 2022). It is developed using Spark modeled fire perimeter inputs, having further hybrid data informing necessary spatial and temporal environmental inputs (weather, topography, vegetation and similar).…”