Weather agent improves alerts with smart learning and reasoning
Agent-Based ML-LLM Fusion with Self-Optimizing Prompts for Plateau Weather Alerts
Artificial IntelligenceMachine Learning
Summary
Weather alerts for tourists often struggle to give clear, reliable, and specific information for mountain areas. The authors built a system called SmartWeatherAgent that understands questions better, predicts hazards more accurately, and creates detailed warnings with reasons and evidence. It combines rules with smart language models and a special machine learning method trained on mountain weather features. The system also improves itself step-by-step, making alerts more trustworthy and easier to understand.
What this means in practice
- •For tourism meteorological services: Generate detailed and context-aware weather alerts specializing in mountain tourism conditions.
- •For emergency management teams: Provide improved hazard warnings with clear explanations and confidence levels for better decision making.
Authors
Shuai Yan, Yang Xu, Shan He
Abstract
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.