Artificial intelligence (AI) enables new approaches to fire behaviour models of operational relevance, including prescribed burns. This is particularly important in modelling of processes that are poorly understood, such as live fuel's effect on fire propagation. The objective of this study was to apply AI algorithms to quantify the effect of the proportion of dead fuels in a senescing grassland, the curing level, on reducing the rate of fire spread relative to the fully cured condition. We applied three different machine learning (ML) models, regression trees, support vector regression (SVR) and Gene expression programming (GEP), two ensemble ML methods, Random Forest and GEP Forest, and non-linear regression analysis to an experimental fire dataset. Results show SVR and GEP as the best ML methods to model the curing level impact on fire spread. No differences in model fit were observed between the best ML methods and non-linear regression analysis.

人工智能 (AI) 为操作相关的火灾行为模型提供了新方法，包括规定的燃烧。这对于对人们知之甚少的过程进行建模尤其重要，例如现场燃料对火灾蔓延的影响。本研究的目的是应用人工智能算法来量化衰老草地中死燃料的比例（固化水平）相对于完全固化条件对降低火势蔓延速度的影响。我们对实验火灾数据集应用了三种不同的机器学习(ML) 模型、回归树、支持向量回归 (SVR) 和基因表达编程 (GEP)、两种集成 ML 方法、随机森林和 GEP 森林以及非线性回归分析。结果表明，SVR 和 GEP 是模拟固化水平对火势蔓延影响的最佳机器学习方法。最佳机器学习方法和非线性回归分析之间没有观察到模型拟合的差异。