标题： 机器学习在野火科学与管理中的应用综述 显示英文标题

标题： A review of machine learning applications in wildfire science and management

摘要： 自20世纪90年代以来，人工智能已被应用于野火科学和管理，早期的应用包括神经网络和专家系统。 自那时以来，该领域迅速发展，与环境科学中机器学习（ML）的广泛采用同步进行。 在此，我们对野火科学和管理中的机器学习进行了范围综述。 我们的目标是提高野火科学家和管理者对机器学习的认识，并说明数据科学家可利用的野火科学中具有挑战性的问题范围。 我们首先介绍了迄今为止在野火科学中常用的流行机器学习方法，然后回顾了它们在野火科学中的应用，涵盖六个问题领域：1）燃料特征、火灾检测和制图；2）火灾天气和气候变化；3）火灾发生、易发性和风险；4）火灾行为预测；5）火灾影响；6）火灾管理。 我们还讨论了各种机器学习方法的优缺点，并在数据科学背景下确定了未来在野火科学和管理中实现进步的机会。 我们确定了298篇相关出版物，其中最常用的机器学习方法包括随机森林、MaxEnt、人工神经网络、决策树、支持向量机和遗传算法。 在野火科学中存在应用更多当前机器学习方法（例如深度学习和基于代理的学习）的机会。 然而，尽管机器学习模型能够自主学习，但野火科学的专业知识对于确保在多个尺度上对火灾过程进行现实建模是必要的，而某些机器学习方法的复杂性要求对其应用需要专业知识。 最后，我们强调，野火研究和管理社区在为使用机器学习方法的从业者提供相关高质量数据方面发挥着积极作用。 显示英文摘要

摘要： Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.