SmartWeatherAgent：高原天气预警系统
Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts
研究人员开发了SmartWeatherAgent，能自动生成包含因果机制、时空演化的结构化天气预警，比传统方法质量提升112%。
SmartWeatherAgent采用三阶段架构，结合意图识别、危险预测和增强生成。系统融合规则方法与大语言模型，使用LightGBM模型处理高原特定特征，在高风、降水和低温事件上达到0.605的F1-Macro分数，延迟仅1.60毫秒。12轮微步骤提示自优化将综合预警质量分数从4.2提升至8.9，提升112%。
Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts
To address insufficient contextualization, weak generalization, and poor scenario adaptation in tourism meteorological services, we propose SmartWeatherAgent--a unified three-stage architecture integrating intent recognition, hazard prediction, and reasoning-enhanced generation. The system fuses rule-based methods with large language models to parse queries at multiple granularities and employs a LightGBM model enriched with highland-specific features (e.g., wind speed abruptness rate), achieving an F1-Macro score of 0.605 with 1.60 ms latency on high-wind, precipitation, and low-temperature events. A 12-round micro-step prompt self-optimization loop boosts the composite warning quality score S_final from 4.2 (B01) to 8.9 (B12, +112%). Key improvements include a sharp rise in B08 from data source citation (6.5 -> 8.5), sustained high performance in B10 via physical mechanism explanation, and a peak scientific rigor score of 9.2 in B12 through explicit uncertainty statements. The system autonomously generates structured warnings that integrate causal mechanisms, spatiotemporal evolution, quantitative evidence, regulatory references, and confidence statements--enhancing professional depth, logical rigor, and scientific soundness, and advancing meteorological services toward proactive perception, explainable decision-making, and intelligent agency.