Allaire Frédéric, Mallet Vivien, Filippi Jean-Baptiste
Institut national de recherche en informatique et en automatique (INRIA), 2 rue Simone Iff, Paris, France; Sorbonne Université, Laboratoire Jacques-Louis Lions, France.
Institut national de recherche en informatique et en automatique (INRIA), 2 rue Simone Iff, Paris, France; Sorbonne Université, Laboratoire Jacques-Louis Lions, France.
Neural Netw. 2021 Sep;141:184-198. doi: 10.1016/j.neunet.2021.04.006. Epub 2021 Apr 20.
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.
野火蔓延的数值模拟有助于预测可能燃烧的区域，并在实际操作中支持决策，特别是在危机情况下和长期规划中。对于短期而言，传统模拟器的计算时间过长，无法在像一个国家或国家的一部分这样的大区域内进行处理，尤其是在火灾危险制图方面。通过模拟在科西嘉岛任何地方点燃火灾并自由蔓延一小时后返回的燃烧表面面积，并考虑广泛的可能环境输入条件，解决了这个问题。使用具有混合架构的深度神经网络来处理两种类型的输入：描述周围景观的空间场和其余的标量输入。在一个大型模拟数据集上进行训练后，该网络在一个补充测试数据集上显示出令人满意的近似误差，平均绝对百分比误差（MAPE）为32.8%。卷积部分是预先计算的，模拟器被定义为网络的其余部分，从而节省了大量计算时间。在一台32核机器上，与模拟器相比，模拟器的加速因子达到数千倍，并且其输入和输出之间的整体关系与野火蔓延的预期物理行为一致。这种计算时间的减少使得能够在不到一分钟的时间内计算出整个科西嘉岛的一小时燃烧面积图，为短期火灾危险制图开辟了新的应用。
