Climate change has resulted in a year over year increase in adverse weather and weather conditions which contribute to increasingly severe fire seasons. Without effective mitigation, these fires pose a threat to life, property, ecology, cultural heritage, and critical infrastructure. To better prepare for and react to the increasing threat of wildfires, more accurate fire modelers and mitigation responses are necessary. In this paper, we introduce SimFire, a versatile wildland fire projection simulator designed to generate realistic wildfire scenarios, and SimHarness, a modular agent-based machine learning wrapper capable of automatically generating land management strategies within SimFire to reduce the overall damage to the area. Together, this publicly available system allows researchers and practitioners the ability to emulate and assess the effectiveness of firefighter interventions and formulate strategic plans that prioritize value preservation and resource allocation optimization. The repositories are available for download at https://github.com/mitrefireline.

翻译：气候变化导致恶劣天气及天气条件逐年加剧，进而引发愈发严重的火灾季。若缺乏有效减灾措施，这些火灾将对生命、财产、生态、文化遗产及关键基础设施构成威胁。为更充分地应对日益加剧的野火威胁，需要更精确的火灾建模工具与减灾响应策略。本文介绍了SimFire——一种通用的野火蔓延模拟器，可生成逼真的野火场景；以及SimHarness——一种基于智能体的模块化机器学习框架，能够在SimFire中自动生成土地管理策略，以减少区域整体损失。这套公开可用的系统使研究人员与从业者能够模拟并评估消防员干预措施的有效性，同时制定优先考虑价值保护与资源优化分配的战略规划。相关代码库可通过https://github.com/mitrefireline 下载获取。