Emulation of wildland fire spread simulation using deep learning

Abstract

Numerical simulation of wildland fire spread is useful to predict the locations that are likely to burn and to support decision in an operational context, notably for crisis situations and long-term planning. For short-term, the computational time of traditional simulators is too high to be tractable over large zones like a country or part of a country, especially for fire danger mapping.This issue is tackled by emulating the area of the burned surface returned after simulation of a fire igniting anywhere in Corsica island and spreading freely during one hour, with a wide range of possible environmental input conditions. A deep neural network with a hybrid architecture is used to account for two types of inputs: the spatial fields describing the surrounding landscape and the remaining scalar inputs.After training on a large simulation dataset, the network shows a satisfactory approximation error on a complementary test dataset with a MAPE of 32.8%. The convolutional part is pre-computed and the emulator is defined as the remaining part of the network, saving significant computational time. On a 32-core machine, the emulator has a speed-up factor of several thousands compared to the simulator and the overall relationship between its inputs and output is consistent with the expected physical behavior of fire spread. This reduction in computational time allows the computation of one-hour burned area map for the whole island of Corsica in less than a minute, opening new application in short-term fire danger mapping.

Multi-input convolutional network for ultrafast simulation of field evolvement

“…However, the merging strategies in existing multi-input ConvNets can suffer from various drawbacks associated with dense parameters. A common merging strategy 30 , 31 , 32 is to flatten the image signal, here represented by its feature maps, into a long vector, making it compatible and, hence, concatenable to the vectorial output of MLP ( Figure 1 E). The flattening-based strategy is suitable and sometimes unavoidable for predicting a single label or numeric value but may not be optimal for ConvNets with an image-type output.…”

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

“…Recent reviews on applications of machine learning to wildfires cover fire susceptibility prediction, fire spread prediction, fuel categorisation, fire occurrence detection, and decision support systems [2], [5], [12]. Deep learning architectures such as convolutional neural networks (CNNs) [4], [10], [19], and recurrent neural networks [7] have also been applied.…”

“…Within the literature on neural networks related to model emulation for fire spread and growth prediction, Allaire et al [4] present a CNN emulator for hazard assessment in a contained region of interest. The emulator predicts the amount of burned land (scalar value).…”

GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress of Artificial Intelligence in Geography

“…Ganapathi Subramanian and Crowley [407] proposed a deep reinforcement learning-based method in which the AI agent is the fire, and the task is to simulate the spread across the surrounding area. As for CNN, the difference between various studies is how they integrate non-image data, such as weather and wind speed, into the model; how they transform these data into image-like gridded data [408]; how they take scalar input and perform feature concatenation [409]; or how they use graph models to simulate wildfire spread [410]. Radke et al [408] combined CNN with data collection strategies from geographic information systems (GIS).…”

“…The atmospheric data are transformed into imagelike data and processed by a 2D CNN network. Allaire et al [409] instead processed the same data, only as scalar inputs. The input scalar was processed by a fully connected neural network into 1024-dimension features and later concatenated with another 1024-dimension features from the input image processed by convolutional operations.…”