融合气象数据与文本叙事，提升脆弱社区的本地风灾预测能力
Interpretable Dual-Stream Learning for Local Wind Hazard Prediction in Vulnerable Communities
- 双流架构融合数值天气与文本事件，实现上下文感知预测
- 在部落社区块级评估中显著优于传统模型
- 通过敏感性分析增强可解释性，适合应急响应决策
美国大平原地区的脆弱社区频繁遭受龙卷风和直线风等风灾，受限于基础设施薄弱和数据覆盖稀疏，现有预报系统难以支持有效应急响应。现有系统多聚焦气象要素，忽视社区特定脆弱性，限制了本地风险评估与韧性规划的应用。为此，本文提出一种可解释的双流学习框架，整合结构化数值天气数据与非结构化文本事件叙述。模型采用随机森林与RoBERTa-based Transformer的后期融合机制，实现鲁棒且情境感知的风灾预测，专为欠发达原住民社区设计，支持块级风险评估。实验表明，该系统显著优于传统基线模型。梯度敏感性分析与消融研究揭示了模型决策过程，提升透明度与实际应用信任度。结果证明其在应急准备与社区韧性建设中的双重价值。
原文摘要 · Abstract (English)
Wind hazards such as tornadoes and straight-line winds frequently affect vulnerable communities in the Great Plains of the United States, where limited infrastructure and sparse data coverage hinder effective emergency response. Existing forecasting systems focus primarily on meteorological elements and often fail to capture community-specific vulnerabilities, limiting their utility for localized risk assessment and resilience planning. To address this gap, we propose an interpretable dual-stream learning framework that integrates structured numerical weather data with unstructured textual event narratives. Our architecture combines a Random Forest and RoBERTa-based transformer through a late fusion mechanism, enabling robust and context-aware wind hazard prediction. The system is tailored for underserved tribal communities and supports block-level risk assessment. Experimental results show significant performance gains over traditional baselines. Furthermore, gradient-based sensitivity and ablation studies provide insight into the model's decision-making process, enhancing transparency and operational trust. The findings demonstrate both predictive effectiveness and practical value in supporting emergency preparedness and advancing community resilience.
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