用大模型整合多维度火灾风险预测，生成可操作报告。
Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis
- 构建多目标预测框架，分别分析气象、点火、干预和资源四类风险。
- 融合大语言模型将分散结果转化为结构化行动建议。
- 适合应急响应人员和消防机构快速获取决策支持。
当前最先进的野火风险评估方法常忽视实际操作需求，限制了其对一线救援人员和消防服务的实际价值。有效的野火管理需要多目标分析，涵盖气象危险性、点火活动、干预复杂性和资源调度等多个维度，而非依赖单一预测指标。本文提出一种概念验证方案，构建一个混合框架：为每个风险维度建立预测模型，并利用大语言模型（LLMs）将异构输出合成结构化、可操作的报告，提升信息整合与决策效率。
原文摘要 · Abstract (English)
Current state-of-the-art approaches to wildfire risk assessment often overlook operational needs, limiting their practical value for first responders and firefighting services. Effective wildfire management requires a multi-target analysis that captures the diverse dimensions of wildfire risk, including meteorological danger, ignition activity, intervention complexity, and resource mobilization, rather than relying on a single predictive indicator. In this proof of concept, we propose the development of a hybrid framework that combines predictive models for each risk dimension with large language models (LLMs) to synthesize heterogeneous outputs into structured, actionable reports.
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